Regulating Ecosystem Services and Green Infrastructure: assessment of Urban Heat Island effect mitigation in the municipality of Rome, Italy

Ecological Modelling 392 (2019) 92–102

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Ecological Modelling journal homepage: www.elsevier.com/locate/ecolmodel

Regulating Ecosystem Services and Green Infrastructure: assessment of Urban Heat Island effect mitigation in the municipality of Rome, Italy

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Federica Marando, Elisabetta Salvatori , Alessandro Sebastiani, Lina Fusaro, Fausto Manes ⁎

Department of Environmental Biology, Sapienza University of Rome, P.le Aldo Moro, 5 - 00185 Rome, Italy

ARTICLE INFO

ABSTRACT

Keywords: Land Surface Temperature Urban Heat Island Urban and peri-urban forests Street trees Climate regulation Nature-Based solution

The Urban Heat Island (UHI) effect is one of the main environmental impacts of urbanization, affecting directly human health and well-being of the city dwellers, and also contributing to worsen environmental quality. As a key strategy to address sustainable urban development, the EU has advocated the development of Nature-Based solutions, such as the implementation of Green Infrastructure (GI), which can deliver a wide range of Regulating Ecosystem Services (ES). In this article, the ES of climate regulation provided by GI has been analyzed in the Municipality of Rome, Italy, characterized by a complex territory and by a Mediterranean climate. The methodological approach allowed to characterize the UHI and to analyze its features in a spatially explicit way and on a seasonal basis, through the Land Surface Temperature (LST) derived from Landsat-8 data. The cooling capacity of different GI elements (peri-urban forest, urban forest, street trees), as well as the effect of vegetation cover and tree diversity on the provision of this regulating ES were assessed. The results show that GI significantly mitigates the hot urban climate during summer, with an effect that is dependent on the GI element and the environmental constrains to which it is exposed. NDVI and tree cover resulted the main indicators of the provision of the ES of climate regulation, highlighting that GI elements such as urban and peri-urban forests have the highest potential to provide this ES in a Mediterranean city. In the context of the Mapping and Assessment of Ecosystems and their Services (MAES) process, our results lend support to claims that GI is important for an ecosystem-based climate adaptation strategy in urban environments, contributing to the definition of knowledge based criteria and indicators, relevant for decision-making in Mediterranean cities.

1. Introduction Urbanization has shown a rapid upward trend during the last sixty years. The global urban population has grown from 751 million in 1950, to 4.2 billion in 2018; due to the overall population growth and to the mass movement of people from rural areas to cities, it is projected to further increase by another 2.5 billion by 2050. In Europe, 74% of population currently lives in cities and towns, making it the third most urbanized region of the world, after North America and Latin America (Koceva et al., 2016). Urban areas in the EU are characterized by high concentrations of economic activities, employment and wealth, but this is often accompanied by high pollution levels and other forms of environmental impact. Indeed, rapid urbanization led to dramatic land consumption, with the conversion of agricultural fields and natural green spaces into artificial surfaces, a phenomenon known as soil sealing (EEA, 2006; Morabito et al., 2017). In this regard, the European Commission has proposed the EU Environment Action Programme to 2020, advocating

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new policies in place in order to halt anthropogenic soil sealing and to achieve “no net land take” by 2050 (European Commission, 2016). By modifying land cover, soil sealing affects matter and energy flow in urban ecosystems, and it is recognized to be one of the main drivers of the Urban Heat Island (UHI) effect. UHI can be described as a distinct urban climate, characterized by higher temperatures (both daytime and night-time) in built-up areas than in the surrounding natural environment (Oke, 1982). Measured UHI intensities for different cities around the world range from 4 °C in Athens and Sidney, up to 12 °C in the megacity of Tokyo (Phelan et al., 2015). UHI is a complex phenomenon, resulting from the different albedo, thermal emissivity, three dimensional configuration and heat capacity of the grey infrastructure (e.g. streets and buildings) in respect to natural and rural land cover (Schwarz et al., 2012), as well as from heat released from anthropogenic sources (i.e. vehicles, residential air conditioners and industries) (Di Leo et al., 2016). UHI can be quantified by measurements of air temperature, Tair, at the “urban canopy level”, i.e, from the ground up to building level. However, Tair is not available in a spatially

Corresponding author. E-mail address: [email protected] (E. Salvatori).

https://doi.org/10.1016/j.ecolmodel.2018.11.011 Received 30 July 2018; Received in revised form 15 November 2018; Accepted 18 November 2018 0304-3800/ © 2018 Elsevier B.V. All rights reserved.

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continuous way within a city, but only in correspondence of a limited number of monitoring sites. In order to analyze temperature distribution over larger areas, an indirect, but spatially explicit method for UHI estimation has been introduced, based on remotely sensed Land Surface Temperature (LST). LST is correlated to Tair in the same sampling site, and can be used for the calculation of the Surface Urban Heat Island (SUHI), a UHI that is assessed through LST (Schwarz et al., 2012). UHI can have many detrimental implications for the urban socioecological system (Phelan et al., 2015), since higher Tair not only affects directly human health and well-being of the city dwellers, particularly during heat waves (Koppe et al., 2004), but it also contributes to worsen environmental quality. It has been found that UHI contributed to around 50% of the total heat-related mortality during the 2003 heat wave in the West Midlands, UK (Heaviside et al., 2016), while in Rome, Italy, a significant increase (16.9%) in mortality of people older than 75 years was observed during the same heat wave (Conti et al., 2005). Furthermore, UHI negatively affects air quality by favoring the formation of photochemical smog, the accumulation of particulate matter due to poor horizontal air dispersion and subsidence (Lai, 2018), increases energy consumption for summer building cooling (Santamouris et al., 2018), and exacerbates heat stress on living organisms, thus affecting biodiversity and ecosystem functioning (Grimm et al., 2008). The European Commission has recently identified the enhancement of sustainable urbanization as a priority target, which can be achieved through the development, within cities, of Nature-Based solutions, i.e. “living solutions inspired by, continuously supported by and using nature” (European Commission, 2015). The concept of Nature-Based Solutions thus brings nature and natural processes into cities, aiming to address environmental and socio-ecological challenges through locally adapted, resource-efficient and systemic interventions (Maes and Jacobs, 2017; Escobedo et al., 2018). One of the most effective Nature-Based Solution for sustainable urban growth is the creation and enhancement of the urban Green Infrastructure (GI), defined as a network of natural, semi-natural and artificial green spaces, delivering a wide range of Ecosystem Services (ES) (European Commission, 2013; Tzoulas et al., 2007). In European cities, the main GI elements are represented by urban and peri-urban forests (i.e. woodlands located inside the city core and in its immediate surroundings, respectively), street trees (i.e. stand-alone trees on roadsides, surrounded by paved ground), and other green spaces such as lawns (Bolund and Hunhammar, 1999). Besides improving air quality through the reduction of gaseous and particulate pollution (Fusaro et al., 2017; Manes et al., 2012; Marando et al., 2016), GI provides significant mitigating effect on UHI and associated health risk (Coronel et al., 2015; Escobedo et al., 2015; Di Leo et al., 2016; Norton et al., 2015). This is known as the ES of climate regulation (CICES Code 2.2.6.2, Haines-Young and Potschin, 2018), and depends on different processes, the most important of which is evapotranspiration (Oke, 1982). Through evapotranspiration, energy from solar radiation absorbed by leaves is converted into latent rather than sensible heat flux, thus lowering canopy temperature, as well as the surrounding air temperature (Bowler et al., 2010; Rahman et al., 2017). GI, and in particular urban forests and trees, can also lower air temperature by intercepting solar radiation, thus preventing the underlying surface to absorb shortwave radiation, a process known as shading effect (Bowler et al., 2010). Based on these processes, air temperature in urban green spaces can be from 1-3 °C up to 5-7 °C cooler than the nearby built-up areas, and this effect can also extend to the surroundings (Cohen et al., 2012; Feyisa et al., 2014; Zhang et al., 2017). Therefore, the enhancement of GI represents an effective mitigation tool to reduce UHI, particularly in hot summer conditions such as those typically occurring under Mediterranean climate (Shashua-Bar et al., 2010; Zardo et al., 2017). Despite the large amount of literature promoting the benefits of GI for climate mitigation, knowledge based criteria and indicators, relevant for decision-making at city-specific scale, need to be further

implemented. A key aspect is represented by the quantification of the mitigating role exerted by different GI elements on UHI, particularly in Mediterranean areas. Indeed, most studies carried out in Mediterranean cities have focused on the cooling effects of single GI elements, such as small parks (Zoulia et al., 2009; Shashua-Bar et al., 2010; Oliveira et al., 2011; Skoulika et al., 2014), while comprehensive analyses across rural-urban transects at city-wide scale are still scarce (Mariani et al., 2016). Furthermore, in order to ensure consistency between GI planning and sustainable urban growth (Capotorti et al., 2017), it is necessary to quantify the effect of vegetation structure and composition on the ES of climate regulation provided urban and peri-urban green areas. In this context, this work aims at: i) characterizing the UHI/SUHI phenomenon in the Mediterranean city of Rome (Italy); ii) assessing the mitigating role that different GI elements, namely urban and peri-urban forests and street trees, exert on SUHI; iii) quantifying the effect of vegetation cover and tree diversity on the provision of this regulating ES. The approach is based on the integration of analyses carried out at different spatial-temporal scale, allowed to assess the UHI/SUHI phenomenon, as well as and the mitigating role of urban GI, across the complex territory that characterizes the Municipality of Rome. 2. Materials and methods 2.1. Study area and vegetation characteristics The Municipality of Rome (41°53′35″N 12°28′58″E, Fig. 1) is the capital of Italy and lies over an area of ∼129,000 ha with a mean altitude of 20 m above sea level. With around 2,872,800 inhabitants, and a population density of approximately 2231 inhabitants/km2 (ISTAT, 2017), it is the most populated municipality of Italy. It is characterized by a heterogeneous territory, due to its complex history and geomorphological features, such as volcanic structures, the Tiber alluvial plain and a coastal plain. It is surrounded by the Tyrrhenian Sea on the southwest, whereas the Central Apennine runs along its northern borders. The climate is Mediterranean, the average temperature is around 15 °C with an average annual rainfall of 839 mm (Blasi, 2001), but extreme events such as heat waves and flooding are becoming more frequent, and may also increase in the future (World Health Organization and United Nations, 2018). The area has undergone intense landscape modifications over time: the urban dispersion phenomenon contributed to a great extent to the decentralization of the city and to an increment in suburban infrastructure (Munafò et al., 2010). Currently, the largest part of the city is covered by agricultural areas (around 55%), followed by sealed cover (22%), but it is nevertheless rich in urban green areas such as historical villas, urban and peri-urban forests and natural reserves, such as the Castelporziano Presidential Estate (6000 ha). The municipality of Rome has an incidence of urban green and protected areas (over 18.7%) above the average if compared to the other Italian cities; furthermore, in the 2011–2014 period, Rome has incremented the surface of urban green spaces by a total of 1.9 million m2 (ISTAT, 2016). 2.2. Conceptual modeling approach Fig. 2 describes the flowchart of the methodology applied in this study. Firstly, we characterized the UHI phenomenon in the Municipality of Rome on a diurnal and nocturnal basis, by calculating UHI indicators from air temperatures data retrieved from available weather stations over the 2013–2017 period (detailed under paragraph 2.3). In parallel, different elaborations on spatially explicit data were performed. The seasonal LST was estimated on the basis of 4 summer and 4 winter Landsat-8 OLI/TIRS images ranging from 2013 to 2017 (Table 1) and a land cover based emissivity map, by means of a model (paragraph 2.4). The spatial characteristics of the SUHI phenomenon were then 93

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Fig. 1. Land cover map of the Municipality of Rome. Urban and peri-urban forests such as Villa Ada Savoia and the Castelporziano Presidential Estate can be observed within the Municipality borders. The NE-SW course of the Tiber River is also evident. The position of the three weather stations considered for Tair analysis, “Via Boncompagni” (urban station), “Castel di Guido” (rural station), and “Castelporziano” (natural station), is marked by red stars. The white dots indicate the position of the polygonal plots in which LST of different Green Infrastructure and urban sites was sampled. Rural-urban transect is shown with a green line, while the Viale Mazzini street trees avenue is indicated by a green rhombus.

analyzed by computing four-year average LST maps for summer and winter, and analyzing LST variation between land uses (“green” and “urban”), as well as along a rural-urban transect (Fig. 1; paragraph 2.5). The cooling capacity of different GI elements (urban and peri-urban forests, street trees) was analyzed by means of a buffer analysis on LST

(paragraph 2.6). Finally, the role of land cover features and structural and functional characteristics of vegetation on the ES of climate regulation was investigated by using a regression model, whose independent variables were obtained from different data sources and ultimately integrated in the sampling (paragraph 2.7). Fig. 2. Flowchart of the conceptual modelling approach. LST: Land Surface Temperature; NDVI: Normalized Difference Vegetation Index; SUHI: Surface Urban Heat Island; UHI: Urban Heat Island; GI: Green Infrastructure; ES: Ecosystem Services. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

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2.3. Urban Heat Island estimation based on air temperatures

2004):

Following Schwarz et al., (2012), a land-cover-driven approach was used to calculate the UHI intensity from air temperature data. This approach determines UHI indicators from air temperatures difference between “urban” and “rural” weather stations, defining a priori “urban” (i.e. built-up surfaces and their surroundings) and “rural” (i.e. agricultural or natural) stations, by means of land cover data. Accordingly, hourly air temperature data (Tair) recoded from 01/01/2013 to 31/12/ 2017 were collected from 3 weather stations, located within the Municipality of Rome in areas characterized by different land cover (Fig. 1). “Via Boncompagni” (AL007, urban station) is located in the city centre (∼35 km from the coast), and “Castel di Guido” (AL004, rural station) is located in a rural area ∼20 km W from the city centre (∼10 km from the shoreline), and both belong to the micrometeorological network of the Regional Agency for Environmental Protection (ARPA Lazio). The third station, “Castelporziano - Castello” (natural station), is instead located around 20 km SW from the city centre (∼8.5 km from the shoreline) within the Castelporziano Presidential Estate, a natural peri-urban forest characterized by several ecosystems (Fares et al., 2009; Manes et al., 1997). Tair displayed a similar temporal trend dynamic in all sites, showing peak values at the beginning of August (around DOY 216-220), and minimum values between mid-December and early February (around DOY 350-40) (see Supplementary Figure S1). In order to derive UHI intensity, hourly Tair differences (ΔTair, °C) between the urban and the rural weather station (ΔTairu- r), as well as between the urban and the natural station (ΔTairu- n), were computed for each year, and then averaged on a daily and nightly basis over the 2013–2017 period.

LST = TB/[1 +

Where K1 and K2 are band-specific thermal conversion constants measured in w/m2 * sr * μm and K, respectively, Lλ is the Spectral Radiance at the sensor’s aperture, measured in w/(m2* sr * μm), and 273.15 is the conversion factor from K to °C. TB was then used for Land Surface Temperature (LST, °C) retrieval according to Eq. (2) (Weng et al.,

2.6. Assessment of summer cooling capacity of different GI elements The mitigating role of different GI elements on summer LST was evaluated by mean of a buffer analysis. Three different GI elements were selected: Castelporziano Presidential Estate as “peri-urban forest”, a natural area of almost 6000 ha, characterized by deciduous and evergreen Mediterranean forests (Manes et al., 1997); Villa Ada Savoia as “urban forest”, one of the largest urban park (180 ha) in the highly urbanized Rome city center, including a fairly well preserved evergreen and deciduous urban forest, that plays an important recreational role for dwellers (Fusaro et al., 2015); Viale Mazzini as “street trees” (480 x 38 m), a Quercus ilex L. tree-lined avenue characterized by elevated vehicular traffic in a highly urbanized context. Consecutive

Table 1 Acquisition dates of the Landsat-8 OLI-TIRS images employed in the study. Winter

27/07/2013 17/07/2015 19/07/2016 07/08/2017

21/12/2014 06/01/2015 26/12/2016 11/01/2017

(2)

To investigate the spatial characteristics of the SUHI phenomenon, average LST maps for the city of Rome were computed for winter and summer. For each season, the four images listed in Table 1 were considered. To produce this map, the average value for each pixel in summer and winter, respectively, was obtained. Furthermore, in order to investigate the effect of vegetation on the SUHI and its inter-annual variability, a stratified random sampling was applied to each image, and LST for “Urban” (N = 149 pixels) and “Green” (N = 149 pixels) areas was collected. These two land covers were identified by means of Corine Land Cover (year 2012). “Urban” corresponds to class 1.1 (urban fabric), 1.2 (industrial, commercial and transport unit) and 1.3 (mine, dump and construction sites), whereas “Green” corresponds to class 3.1 (forests) and 1.4.1 (green urban areas). Finally, the summer and the winter images for the year 2016 were used to analyze LST variation along a rural-urban (SW-NE) transect (samples distance: 60 m), ranging from the coast to the northern part of the city (Fig. 1). The SW-NE direction was selected in order to take into account different GI elements (i.e. the large peri-urban forest of the Castelporziano Estate, and a urban forest, Villa Ada Savoia), as well as agricultural lands, different urban textures and water bodies. The year 2016 was chosen because, on average, its LST falls within a mean range for the study area.

(1)

Summer

× ln( )

2.5. Spatial analysis of SUHI

Eight Landsat 8 OLI-TIRS images for the area of the Municipality of Rome (path/row 191/31, WGS84 UTM33 N reference system) were downloaded from the United States Geological Survey (USGS) website. Cloud free images were acquired for a time period ranging from July 2013 to August 2017 in summer and winter months (Table 1), in order to investigate seasonal trends since SUHI effect varies across seasons (Imhoff et al., 2010), and to allow comparison amongst different years. The elaborations were performed with different open-source software (QGIS, GRASS GIS); radiometric calibration and correction for atmospheric effects through Dark Object Subtraction (DOS1) were performed through the Semi-Automatic Classification Plugin implemented in QGIS (SCP, Congedo and Macchi, 2015), in order to retrieve surface reflectance. Following the methodology developed by Weng et al. (2004), band 10 (30 m spatial resolution) was then converted to at-satellite brightness temperature (TB, °C) following Eq. (1) (USGS, 2018):

273.15

TB c2

Where is the wavelength of the emitted radiance, c2 is the Planck’s second radiation constant (1.4388 × 10−2 mK) and is the land surface emissivity. The emissivity values for were retrieved from literature data (Li et al., 2004; Mallick et al., 2012) and applied to different land cover classes (Weng et al., 2004; Sheng et al., 2017). In particular, 4 land cover classes were identified: built-up, vegetation, water bodies and bare soil areas. Built-up areas were identified using the national soil consumption dataset developed by ISPRA for the year 2016, at a spatial resolution of 10 m. Such dataset is based on the Copernicus High Resolution Layer for imperviousness, and further improved in resolution and thematic accuracy including other elements of soil consumption through a combination of semi automatic high resolution satellite classification and local datasets (Copernicus, 2018; ISPRA, 2018). To identify the other land cover classes, after conversion to surface reflectance, bands 4 and 5 of two Landsat 8 OLI-TIRS images (for summer and winter 2016, since no significant changes in land cover classes are expected in the period ranging from 2013 to 2017) were used to calculate NDVI as (NIR - R)/(NIR + R). Vegetated areas were located where a threshold value of 0.3 was exceeded. This threshold was chosen following Weier and Herring (2011); Esau et al. (2016) and Telesca and Lasaponara (2006). Accordingly, areas with positive NDVI values lower than 0.3 were then assigned to the class of bare soil areas. Water bodies were identified for NDVI values below 0.

2.4. Land Surface Temperature (LST) retrieval model

TB = K2/ln[(K1/ L ) + 1]

×

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Fig. 3. UHI effect in the city of Rome. Average ± S.D. of daytime (a, c) and nighttime (b, d) air temperature differences (ΔTair) over the 2013–2017 period, between the Urban (“Via Boncompagni”) and Rural (“Castel di Guido”) sites (a, b), and the Urban (“Via Boncompagni”) and Natural (“Castelporziano”) sites (c,d). Data from the micrometeorological network of the ARPA Lazio (“Via Boncompagni” and “Castel di Guido”), and from the “Castello” weather station of the Castelporziano Presidential Estate (“Castelporziano”).

buffers of 10 m width were used to analyze LST variation from the boundary of each GI element to a maximum distance of 300 m towards the built up area. Then, the LST average difference between each buffer and the GI inner core (ΔLST, °C) was calculated for the four summer images (years 2013–2017, Table 1). The cooling capacity of each GI element was then evaluated, both in terms of LST decrease (°C) and maximum cooling distance from the boundary of the GI element, by identifying the first turning point of the buffer temperature (Yu et al., 2017).

a regular grid composed by cells of one hectare size each was used to sample different variables that affect LST in the nine plots. The considered variables were the following: distance from the city centre (C_dist, m), conventionally located at the representation office of the Municipality of Rome (Campidoglio Square, 1, 41°53′34.7″N 12°29′00.8″E), and distance from the Tiber river (R_dist, m) (retrieved from Open Street Map dataset); surface covered by urban settlement (m2 urban), calculated on the basis of the ISPRA soil consumption dataset (ISPRA, 2018); surface covered by trees (m2 trees), and number of different tree functional groups (FG) (evergreen broadleaves, deciduous broadleaves and conifers) in each cell of vegetated area, both obtained from a Sentinel-2 land cover classification modified from Fusaro et al. (2017); Normalized Difference Vegetation Index (NDVI) of the vegetated areas (for NDVI values > 0.3), estimated for each year and season from the same Landsat-8 OLI/TIRS images used to retrieve LST.

2.7. Assessment of factors affecting the ES of climate regulation provided by GI Nine polygonal plots of 180 ha each, characterized by different features in terms of land cover textures and position inside the metropolitan area, were sampled across the Municipality (Fig. 1). Five plots include mainly built-up areas with different urbanization density: “Salario” and “City centre” are two dense-built up areas located in the central part of the Municipality, “Termini” is a highly urbanized neighborhood characterized by the presence of the main railway station of Rome, “Infernetto” and “Olgiata” are residential areas made up of detached and semi-detached houses and small green areas. The other four polygonal plots include vegetated and agricultural areas: “Marcigliana” is located in the North-Eastern sector of the city, in a rural area including cultivated and uncultivated lands as well as patches of natural vegetation; “Villa Ada” was chosen in correspondence of the Villa Ada Savoia, an urban forest surrounded by a deeply urbanized area that includes the “Salario” urban plot; other two plots, named “Castelporziano 1″ and “Castelporziano 2″ are located inside the large Castelporziano peri-urban forest. Average summer and winter LST (°C) in the different polygonal plots are reported in Supplementary Table 1. In order to identify the factors that explain LST variation by season,

2.8. Statistical analysis LST data were analyzed through the Statistica v 7.0 (StatSoft, Inc., Tulsa OK, USA) software. The difference between “Urban” and “Green” land covers was investigated by means of a one-way analysis of variance (ANOVA). Year and land cover effects on LST were then tested with Repeated Measures ANOVA. A multivariate Regression analysis through stepwise regression (p < 0.05), was applied to data obtained from the polygonal plots, using LST as dependent variable and the following input variables as independent factors: C_dist, R_dist, m2 urban, FG, m2 trees, NDVI.

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3. Results and discussion

since Landsat 8 images in the Rome area are acquired around 10:00 AM both during summer and winter. Therefore, beyond land use types, geographical location, geomorphological features, climatic situation, seasons and times of day should be taken into account when performing satellite analysis of SUHI. On the other hand, the lowest LST values are displayed by large green areas such as the peri-urban forest of Castelporziano Presidential Estate (around 28 °C) and urban parks inside the city, such as the urban forest of Villa Ada (around 32–33 °C). As for average winter LST (Fig. 4b), it is interesting to highlight that no clear SUHI effect was evident, coherently with what observed using Tair measured by weather stations (Fig. 3). Winter LST values range from around 5 to 17 °C, with most values ranging from around 8.5 to 11 °C, temperatures that are displayed by large areas of the urban texture. Values up to 15–17 °C can again be found in correspondence of bare soils, annual crops and coastal sandy dunes, whereas values for urban and peri-urban forests are generally lower, ranging from 5 °C in the northern part of the city, to 8–9 °C in natural areas such as Castelporziano, where the proximity of the sea can mitigate cold winter temperatures.

3.1. Characteristics of UHI and SUHI in the municipality of Rome 3.1.1. UHI estimation through air temperatures The air circulation in the Municipality of Rome during high pressure summer conditions is dominated by the sea-land breeze regime typical of Mediterranean coastal areas (Fares et al., 2009), which is known to interact with UHI in a complex way (Cenedese and Monti, 2003; Nastran et al., 2018). In spring and summer, during the day, Tair of the urban station was, on average, around 1 °C higher than the rural and natural ones (Fig. 3a, c). This UHI magnitude is in the range of what previously described for other coastal Mediterranean cities: in Thessaloniki, Greece, Giannaros and Melas (2012) reported that maximum UHI intensity ranged from 2 to 4 °C during the warm part of the year, while in Barcelona, Spain, Salvati et al. (2017) reported a maximum average UHI around 1.7 °C in summer. Interestingly, such UHI was even more evident by considering nighttime Tair (Fig. 3b, d), despite the nocturnal air movement of the study area is dominated by land-sea breeze (Fares et al., 2009). In particular, average nocturnal Tair recorded in the urban site were 1.85 and 2.15 °C higher than that measured in the rural station in spring and summer, respectively (Fig. 3b), and this difference was even higher considering the natural site of Castelporziano (2.81 and 3.17 °C in spring and summer, respectively, Fig. 3d). The nocturnal UHI is known to exert a stronger detrimental effect on human wellbeing and building energy use than the diurnal one (Zhang et al., 2017). Fig. 1 allows to highlight the different presence and extension of the peri-urban forests between the site adjacent to the Castel di Guido weather station and that of Castelporziano. It is possible to hypothesize that the differences of temperature observed for these two sites with the urban station, which are higher for the Castelporziano site, can be attributed to the regulating ES of climate mitigation provided by vegetation (Mariani et al., 2016). Therefore, the nocturnal cooling service provided by the large Castelporziano periurban forest during hot summer months (particularly July and August) appears of particular importance (Vaz Monteiro et al., 2016). As for winter months, Tair of the Castel di Guido rural site was, on average, similar, or slightly higher, than that of the urban site during both night and day (average ΔTairu-r = 0.06 and 0.45 °C for day and night, respectively). Considering the natural site of Castelporziano as reference, instead, the nighttime UHI was also evident during winter (average ΔTair u-n = 0.13 and 1.08 °C for day and night, respectively), coherently with what reported for Thessaloniki, Greece (Giannaros and Melas, 2012).

3.2. Land-cover based analysis of LST 3.2.1. Effect of vegetation on LST Mean summer LST for “Green” pixels (Table 2a), ranged from 32.36 ± 2.21 °C for the year 2013 to 33.43 ± 2.60 °C for the year 2015, with an average summer value of 33.10 ± 2.99, whereas “Urban” areas average summer LST ranged from 36.21 ± 2.53 °C for the year 2017 to 40.87 ± 2.48 °C for the year 2015, with an average summer value of 40.48 ± 2.92 °C. As regards winter temperatures, “Green” areas mean LST range from 4.45 ± 0.87 °C for the year 2017 to 12.01 ± 1.04 °C for the year 2014, and an average winter LST equal to 9.08 ± 3.10 °C. Average winter LST for “Urban” areas show a minimum of 5.64 ± 1.40 °C in the year 2017 and a maximum of 12.98 ± 1.47 °C in the year 2014, and an overall LST of 9.91 ± 3.15 °C for the winter season. “Green” areas display significantly lower temperatures than “Urban” areas (p < 0.05) for all the considered years and for both the overall summer and winter seasons. From the repeated measures ANOVA (Table 2b), a significant effect of the year, and year*Land cover is also highlighted (p = < 0.001), showing that LST values differ significantly in relation to different years and land cover. This is in accordance to what pointed out previously by Yuan and Bauer (2007), who found that the percentage of impervious surface has a strong linear relation with LST for all the seasons, while Sheng et al. (2015) reported that satellite-based LST is significantly influenced by the presence of vegetation. This result suggest the importance of the green areas in the mitigation of the urban climate, and in a broader sense, to improve environmental quality and human wellbeing, particularly during hot summer conditions that are typical of Mediterranean cities (Diffenbaugh et al., 2007).

3.1.2. Spatial characteristics of the SUHI SUHI is widely used as a proxy of UHI since, although absolute values are not comparable due to the different data acquisition and information content of the two indicators (satellite-derived LST vs ground-based Tair, respectively), many studies have shown that SUHI and UHI are significantly correlated (Chen et al., 2012; Feyisa et al., 2014; Schwarz et al., 2012). SUHI can therefore be used to analyze the spatial characteristics of urban heat island, and the cooling capacity of GI within a city. Average summer LST values (Fig. 4a) range from 27.5 to 50.2 °C. In correspondence of the most sealed surfaces in the city centre and in residential and industrial areas, LST values range from around 39 to 43 °C. The highest values (up to 50.2 °C) can be found for bare soils and uncultivated lands, particularly present in the Eastern part of the Metropolitan area (Fig. 1). Previous studies have shown that, in arid and semi-arid cities, bare soils (i.e. agricultural soils and sandy surfaces with no vegetation cover) around the urban area can show higher LST than the urban surface itself, especially during the morning (Haashemi et al., 2016; Lazzarini et al., 2013). This effect is due to the low heat capacity and conductivity of such soils, which are heated more quickly by the morning sun than the artificial surfaces within the urban area. We argue that this phenomenon has happened in our study too,

3.2.2. Rural-urban transect Fig. 5 shows the LST along the Rural-Urban transect (38 km, see Fig. 1) for both summer (in red, left axis) and winter (in blue, right axis). Such remotely sensed LST provided as spatially continuous data over a whole city or region, allows highlighting the relations between the urban area and the surrounding landscape. This is particularly important for studying the UHI, since both the temperature drivers of the urban area and of the surrounding landscape must be taken into account (Heinl et al., 2015). The summer transect clearly highlights the UHI phenomenon, showing a progressive increase of LST from the SW natural and agricultural zones, towards the city centre (Hidalgo et al., 2008). LST values range from 26 to 32 °C in correspondence of the Castelporziano peri-urban forest, to 36–45 °C in the built-up area, with a maximum difference of around 15–20 °C in LST between peri-urban and urban areas. In the city centre (between 30–35 km from the starting 97

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Fig. 4. Maps of summer (a) and winter (b) LST (°C). Average LST of four years for each season is presented (2013, 2015, 2016, 2017 for summer; 2014, 2015, 2016, 2017 for winter).

transect during summer suggest that vegetation, and in particular urban and peri-urban forests, exert a greater impact on SUHI mitigation compared to winter. As already shown in Fig. 4, bare soil areas located from 10 to 5 km SW and NE from the built-up areas, display higher LST than urban settlements for both seasons.

Table 2 LST of considered years presented as Mean ± S.D. Outputs of One-way ANOVA (p < 0.05) to test the effect of land cover on LST for each year is reported, as well as the N (a). Repeated Measures ANOVA (b) was carried out for analyzing the effect of time (year) and its interaction with land cover. Significant p-values (p < 0.05) are marked in bold. a) One-way ANOVA

3.3. Summer cooling capacity of different GI elements

Sampled pixels Date 27/07/2013 Summer 17/07/2015 19/07/2016 07/08/2017 Average summer 21/12/2014 Winter 06/01/2015 26/12/2016 11/01/2017 Average winter

Green Urban LST Mean ± S.D. LST Mean ± S.D. p (°C) (°C)

N

32.36 ± 2.21 33.43 ± 2.60 32.02 ± 2.84 34.60 ± 3.50 33.10 ± 2.99 12.01 ± 1.04 8.94 ± 1.32 10.92 ± 1.15 4.45 ± 0.87 9.08 ± 3.10

149 149 149 149 596 149 149 149 149 596

39.16 ± 2.45 40.88 ± 2.47 39.29 ± 2.58 42.59 ± 2.78 40.48 ± 2.92 12.98 ± 1.47 9.27 ± 1.49 11.69 ± 1.50 5.64 ± 1.40 9.91 ± 3.15

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.050 < 0.001 < 0.001 < 0.001

The three considered GI elements, namely peri-urban forest (Castelporziano Presidential Estate), urban forest (Villa Ada Savoia) and street trees (Viale Mazzini), display a different cooling capacity during summer, both in terms of temperature decrease of the surrounding urban area, and of cooling distance from the boundary of the GI element (Fig. 6). The large peri-urban forest and the urban forest were, in the considered years, 2.5–3.1 °C and 2.8–3.2 °C cooler (first 10 m buffer) than their surroundings, respectively. This cooling intensity is in the range of what reported for other Mediterranean cities, such as Tel Aviv (Potchter et al., 2006; Cohen et al., 2012) and Athens (Zoulia et al., 2009; Skoulika et al., 2014), thus supporting the conclusion that GI are highly efficient in mitigating summer urban heat island under Mediterranean climatic conditions (Zardo et al., 2017). The buffer analysis also highlights that, for Castelporziano, the cooling effect on LST extends up to 170 m, with no relevant differences between years (Fig. 6a), indicating a high functional stability of the large periurban forest. Despite this is a relatively short-distance effect, coherent with previous works considering LST (Yu et al., 2017), it must be underlined that the corresponding effect on air temperature could be higher: indeed, for a much smaller park (6 ha) in Athens, Skoulika et al. (2014) reported a cooling effect on Tair extending up to 300 m. As for the Villa Ada urban forest, the maximum cooling distance of this GI element shows noticeable inter-annual differences: it extends up to 100 m during 2013, 2015, and 2016, while a turning point in the ΔLST is already evident 50 m away from the park boundaries during 2017 (Fig. 6b). This lower cooling effect can be explained by considering that, during summer 2017, Central Italy has been affected by an intense drought and heat wave, which caused diffuse damages to

b) Repeated measures ANOVA Year Year * Land cover

p < 0.001 < 0.001

point of the transect), large decreases in LST can be observed in correspondence to the Villa Ada urban forest as well as to water bodies. As regards the winter transect, no clear heat island effect is evident, and temperature variations between rural and urban areas are less pronounced. It is noteworthy that LST within the Castelporziano Presidential Estate tends to increase from coastline towards inlands during summer, while an opposite trend is observed in winter, suggesting a mitigating role on cold air temperatures carried out by the sea (Sakakibara and Owa, 2005). More marked LST fluctuations along the 98

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Fig. 5. LST sampled along a linear transect (from SW to NE, see Fig. 1). Lines on the top of the graph represent: Castelporziano peri-urban forest (P-UF) and Villa Ada urban forest (UF) (green lines), bare soil areas (BS) and sandy dunes (SD) (orange line), built-up areas (BU) (grey lines) and water bodies (W) (blue lines). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.

forest ecosystems (i.e. early foliar shedding and desiccation of leaves and branches) (Pollastrini et al., 2018). Besides the direct effects of severe drought on the evapotranspiration potential and on leaf area production (i.e. reduced NDVI) (Manes et al., 1997), such damages

reduce both the evaporative cooling and the canopy shading capacity of trees. It can be argued that, differently from what observed for the natural peri-urban forest of Castelporziano, the stressful conditions of the urban environment (i.e. higher Tair and VPD, presence of air

Fig. 6. Cooling capacity of different GI elements during summer in the Metropolitan City of Rome: a) Peri-Urban Forest (Castelporziano Presidential Estate); b) Urban Forest (Villa Ada Savoia); c) Street trees (Viale Mazzini). The average summer LST difference between each 10 m consecutive buffers and the GI inner core (ΔLST, °C) is showed for each of the considered years (2013–2017). Vertical lines indicate the first turning point of ΔLST, i.e. the cooling capacity. 99

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pollutants, shallow soils with lower water availability) may have further reduced the functionality of the urban forest (Fusaro et al., 2015), thus exacerbating the negative effects of the summer 2017 heat wave. In this context, management practices aimed at enhancing soil water availability, for example by an appropriate irrigation schedule in urban parks, or by the amelioration of soil structure and texture in newly planned GI (Mariani et al., 2016), could substantially help in increasing the climate regulation potential of urban GI. This is particularly relevant in Mediterranean environments where, during summer months, water availability already represents a limiting factor for vegetation growth and functionality (Manes et al., 1997). Finally, the street trees of Viale Mazzini Avenue are around 1.3 °C cooler than the first 10 m buffer of built-up area, and their influence is extended up to 30 m from the borders of this linear GI element, with no difference between years (Fig. 6c). This lower cooling capacity in respect to that of urban and peri-urban forests could be due to different factors, such as the smaller size of the GI element or its lower canopy cover (i.e. lower NDVI, data not shown) (Yu et al., 2017). Furthermore, while urban and peri-urban forests grow on unpaved soils, street trees have most of their roots confined in a small planting pit, surrounded by sealed soils. Indeed, impermeable pavements reduce evaporative cooling from soil, potentially decreasing also plant transpiration of adult trees (Fini et al., 2017), thus directly affecting the cooling capacity of the GI element. It is, however, worth to underline that the cooling effect of street trees, although small, is not negligible, appearing important for improving local microclimate in highly urbanized neighborhoods within the city core (Shashua-Bar et al., 2010), particularly during the hot Mediterranean summer conditions.

the best predictive performance, followed by the surface covered by trees (m2 trees, R2 stepwise increase = 0.066). These two variables showed a negative relationship with LST, indicating that the more trees with high canopy cover in a green area, the more effective it will be in providing the ES of climate regulation. Indeed, previous studies have shown that LST is strongly correlated with NDVI, as well as with other vegetation indices linked to canopy cover, such as Leaf Area Index (Hardin and Jensen, 2007). GI elements such as urban and peri-urban forests have therefore the highest potential to provide the ES of climate regulation in urban areas, if compared to other vegetation types such as meadows or hedges (Skelhorn et al., 2014). The distance from the city center (C_dist) is another important factor showing a negative relationship with summer LST (R2 stepwise increase = 0.039), suggesting that, due to the UHI phenomenon, green areas closer to the urban core should be particularly efficient, in order to provide the ES of climate regulation. Since the demand for this ES is higher in the city centre, were both population and urbanization density are highest (Lafortezza and Giannico, 2017), this aspect is of particular policy-relevance, and should be taken into account for planning new GI elements, targeted at mitigating UHI in the different sectors of the city of Rome. The distance from the Tiber river (R_dist), and the urban surface cover (m2 urban) have instead a very little influence on summer LST (R2 increase: 0.002 and 0.001, respectively), while the number of tree functional groups (FG) in each cell is not significant at all (R2 increase: 0). This latter results is of some interest: previous studies have in fact shown that the provision of several Regulating ES, such as the air quality improvement, by GI, is positively affected by tree diversity, particularly under Mediterranean climatic conditions (Manes et al., 2012, 2016). Although further studies are needed to better elucidate the relationship between diversity level and ES (Mori et al., 2017), our result seems to suggest that this relationship may not be so determinant for the provision of the ES of climate regulation by urban GI, at least in the city-specific conditions of the studied area. As for the winter model, its predictive capacity on LST, although significant (p < 0.01), was very low (R2= 0.091, Table 3b). This result confirms the absence of a daytime UHI in winter, as already highlighted by the analysis of Tair (Fig. 3a, c), by the spatial analysis of LST (Fig. 4b), as well as by the rural-urban transect (Fig. 5). Sheng et al. (2017) have also found such a difference between summer and winter, suggesting that the summer UHI/SUHI phenomenon may be predominant under several climatic conditions.

3.4. Role of land cover features and vegetation characteristics on the ES of climate mitigation Stepwise multiple-linear regression was applied to investigate how changes in land cover features, obtained from the polygonal plots, are able to describe the seasonal changes in LST observed in the Municipality of Rome in the 2013–2017 period. The results show a high predictive capacity for the summer model, with a determination coefficient scored at 0.75 (p < 0.01, Table 3a). In this model, NDVI owns Table 3 Multiple regression model outputs for summer (a) and winter (b) season. Regression summary for dependent variable LST are detailed: intercept, coefficient of determination, R2, and model p value. Regression coefficient β, its standard error (S.E.) and the change in R2 for each steps are reported. Significant factors are marked in bold.

4. Conclusions In the context of the Mapping and Assessment of Ecosystems and their Services (MAES) process (Maes et al., 2016), this work has investigated the ES of climate regulation (CICES Code 2.2.6.2, HainesYoung and Potschin, 2018), delivered by GI in the Metropolitan area of Rome, Italy. Our integrated approach has shown that a UHI phenomenon is evident in Rome during the hot Mediterranean summer conditions, and such summer UHI can be mitigated by GI. The cooling capacity differed between the considered GI elements, with peri-urban forest showing the highest temperature reduction and cooling distance, followed by urban forest, and street trees. At this regard, specific management policies appear necessary for a cost effective improvement of the cooling capacity of GI elements located closer to the urban core, were the demand for the ES of climate regulation is higher, but plant functionality can be limited by environmental constraints (Fusaro et al., 2015). These include appropriate irrigation schedules, as well as the amelioration of soil structure and texture in the newly planned GI. On the same time, considering the continuous growth of urban areas towards their rural surroundings (Catalàn et al., 2008), the importance of peri-urban forests in terms of ES provision should be also recognized, preserving and expanding them as possible, in view of a sustainable urban growth. Finally, the multiple-linear regression model have showed that firstly the NDVI, and then the surface covered by trees, that

a) Summer Intercept 41.31; R2 0.75; p < 0.001

NDVI m2 trees C_dist R_dist m2 urban FG

β

S.E.

R2 change

−0.417 −0.371 −0.286 0.093 0.042 0.024

0.014 0.011 0.015 0.014 0.009 0.013

0.643 0.066 0.039 0.002 0.001 0

β

S.E.

R2 change

−0.098 0.069 0.165 −0.121 0.117

0.023 0.026 0.017 0.025 0.027

0.062 0.009 0.015 0.004 0.003

b) Winter Intercept 8.24; R2 0.091; p < 0.001

FG R_dist m2 urban NDVI C_dist

100

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affect evapotranspiration and shading, are the main indicators of the provision of the ES of climate regulation by GI, highlighting again that GI elements such as urban and peri-urban forests, have the highest potential to provide this ES in a Mediterranean city. Interestingly, differently from what reported for other Regulating ES, climate regulation resulted unaffected by tree diversity, at least in the city-specific conditions of the studied area during the summer season. Our results lend support to claims that GI is important for an ecosystem-based climate adaptation strategy in urban environments, contributing to the definition of knowledge based criteria and indicators, relevant for decision-making in Mediterranean cities. At this regard, some limitation of this study have to be highlighted, which need to be addressed in future researches. The most important is the lack of identification of the minimum sized treed area that is needed to effectively reduce temperature to acceptable levels for human wellbeing. In fact, this quantification would have required the analysis of many different GI elements with different sizes, from very small to very large, that are not available in our study area. Due to the use of satellitederived LST in this study, it has been also impossible to quantify the mitigating effect of GI on the nocturnal summer UHI, which has resulted particularly important for the city of Rome. For this quantification, Tair data measured in a spatially continuous way are required, thus highlighting the need to implement existing weather monitoring network within the city, as well as in its surroundings. Finally, the role of tree diversity in the provision of ES of climate regulation should be further investigated, also considering other Mediterranean cities as case-study.

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Acknowledgments This research was supported by the following grants: Project: "Global Change and Health in the Vision “Planetary health”, coordinated by ISS, funded by Italian Ministry of Health (Capitolo 4100/ 22); Project “Enhancing Resilience Of Urban Ecosystems through Green Infrastructure” (EnRoute) funded by the Joint Research Centre of the European Commission, 12/2016–11/2018. We thank Ing. Massimo Magliocchetti from ARPA Lazio and the Direction of the Castelporziano Presidential Estate for climatic data provisioning. We are grateful to Prof. Giuseppe Raspa, Sapienza University of Rome, for his suggestions regarding statistical analyses. We also thank the two anonymous Referees for their constructive comments and suggestions that helped us to improve the quality of this work. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ecolmodel.2018.11. 011. References Blasi, C., 2001. Carta del fitoclima dell’area romana (1:100.000). Inf. Bot. Ital. 33, 240–243. Bolund, P., Hunhammar, S., 1999. Ecosystem services in urban areas. Ecol. Econ. 29 (2), 293–301. https://doi.org/10.1016/S0921-8009(99)00013-0. Bowler, D.E., Buyung-Ali, L., Knight, T.M., Pullin, A.S., 2010. Urban greening to cool towns and cities: a systematic review of the empirical evidence. Landsc. Urban Plan. 97, 147–155. https://doi.org/10.1016/j.landurbplan.2010.05.006. Capotorti, G., Alós Ortí, M.M., Copiz, R., Fusaro, L., Mollo, B., Salvatori, E., Zavattero, L., 2017. Biodiversity and ecosystem services in urban green infrastructure planning: a case study from the metropolitan area of Rome (Italy). Urban For. Urban Green. https://doi.org/10.1016/j.ufug.2017.12.014. Catalàn, B., Saurì, D., Serra, P., 2008. Urban sprawl in the Mediterranean? Patterns of growth and change in the Barcelona Metropolitan Region 1993–2000. Landsc. Urban Plan. 85, 174–184. https://doi.org/10.1016/j.landurbplan.2007.11.004. Cenedese, A., Monti, P., 2003. Interaction between an inland urban heat island and a seabreeze flow: a laboratory study. J. Appl. Meteorol. 42 (11), 1569–1583. https://doi. org/10.1175/1520-0450(2003)042. Chen, X., Su, Y., Li, D., Huang, G., Chen, W., Chen, S., 2012. Study on the cooling effects of urban parks on surrounding environments using Landsat TM data: a case study in Guangzhou, southern China. Int. J. Remote Sens. 33, 5889–5914. https://doi.org/10.

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