Published January 11, 2016

Journal of Environmental Quality

Special Section The Urban Forest and Ecosystem Services

Modeling Tree Shade Effect on Urban Ground Surface Temperature Marco Napoli, Luciano Massetti,* Giada Brandani, Martina Petralli, and Simone Orlandini

U

rban climate modifications due to anthropogenic

Abstract

changes in land cover and built forms have been studied extensively all around the world (Grimmond et al., 2009; Petralli et al., 2014; Middel et al., 2014). Such studies can have applications in several fields, from urban planning and design to citizens’ health and behavior. In recent decades, many studies have focused on the effect of urban planning on urban heat island effect, thermal comfort, and citizen health and wellbeing (Lindberg and Grimmond, 2011). For this reason, heat storage and surface temperature (Ts) of materials commonly used in urban pavements have also been investigated (Asaeda et al., 1996; Arnfield, 2003; Doulos et al., 2004). However, the heat surface exchange processes at the local and city scale is a complex problem that needs other approaches, such as thermal infrared satellite imagery or numerical modeling. Recently, the number of models able to simulate the heat surface exchange between urban cover and atmosphere has increased significantly (Krayenhoff et al., 2014). These vary from those considering the urban environment as a homogeneous concrete surface to more complex models that consider all details, taking into account energy fluxes within the atmospheric boundary layer (Shaffer et al., 2015; Masson, 2000). The inclusion of vegetation in models is critical for the proper simulation of the local energy balance (Lemonsu et al., 2012). Soil and vegetation can moderate the local micro-climate by slowing the release of water through evapotranspiration (Grimmond and Oke, 1991; Krayenhoff et al., 2014). In addition, trees offer shade to the pedestrians and ground surfaces, which can lead to a decrease of the radiant temperature and Ts (Ca et al., 1998; Simpson, 2002; Lindberg and Grimmond, 2011). Tree shade effect on Ts varies according to tree species and type of surface material. As Oke (1987) reports, one of the

There is growing interest in the role that urban forests can play as urban microclimate modifiers. Tree shade and evapotranspiration affect energy fluxes and mitigate microclimate conditions, with beneficial effects on human health and outdoor comfort. The aim of this study was to investigate surface temperature (Ts) variability under the shade of different tree species and to test the capability in predicting Ts of a proposed heat transfer model. Surface temperature data on asphalt and grass under different shading conditions were collected in the Cascine park, Florence, Italy, and were used to test the performance of a one-dimensional heat transfer model integrated with a routine for estimating the effect of plant canopies on surface heat transfer. Shading effects of 10 tree species commonly used in Italian urban settings were determined by considering the infrared radiation and the tree canopy leaf area index (LAI). The results indicate that, on asphalt, Ts was negatively related to the LAI of trees (Ts reduction ranging from 13.8 to 22.8°C). On grass, this relationship was weaker probably because of the combined effect of shade and grass evapotranspiration on Ts (Ts reduction ranged from 6.9 to 9.4°C). A sensitivity analysis confirmed that other factors linked to soil water content play an important role in Ts reduction of grassed areas. Our findings suggest that the energy balance model can be effectively used to estimate Ts of the urban pavement under different shading conditions and can be applied to the analysis of microclimate conditions of urban green spaces.

Core Ideas • A heat transfer model with a routine for estimating the tree canopies effect was proposed. • Surface temperature of lawn and asphalt under different trees shading conditions were measured. • The model provided good predictions of the temperatures for lawn and asphalt under different trees.

M. Napoli, G. Brandani, M. Petralli, S. Orlandini, Dep. of Agrifood Production and Environmental Sciences, Univ. of Florence, piazzale delle Cascine 18, 50144 Florence, Italy; L. Massetti, Institute of Biometeorology, National Research Council, via G. Caproni 8, 50145 Florence, Italy Florence, Italy; G. Brandani, M. Petralli, S. Orlandini, Interdepartmental Centre of Bioclimatology, Univ. of Florence, Florence, Italy, piazzale delle Cascine 18, 50144 Florence, Italy. Assigned to Associate Editor Carlo Calfapietra.

Copyright © 2015 American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. 5585 Guilford Rd., Madison, WI 53711 USA. All rights reserved.

Abbreviations: ACE, Acer negundo L.; AES, Aesculus hippocastanum L.; CED, Cedrus deodara G. Don.; CEL, Celtis australis L.; CIres, 95th percentile confidence interval of residuals analysis; IR, infrared thermometer; LAI, leaf area index; LIG, Ligustrum lucidum W.T. Aiton; M%E, mean percentage error; NSC, Nash–Sutcliffe coefficient; OLE, Olea europaea L.; PIN, Pinus pinea L.; PLA, Platanus occidentalis L.; PRU, Prunus cerasifera Ehrh. var Pissardii; RSR, ratio of the root mean square error to observation standard deviation; TIL, Tilia cordata L.

J. Environ. Qual. 45:146–156 (2016) doi:10.2134/jeq2015.02.0097 Received 13 Feb. 2015. Accepted 2 Oct. 2015. *Corresponding author ([email protected]).

146

main factors involved in Ts is the shade provided by trees mostly because of the interception of solar radiation, preventing the warming of the ground and, consequently, of the air. Moreover, as Abreu et al. (2012) report, within tree species, the types of canopies must be taken into consideration because each tree’s characteristics result in different capacities of attenuation of solar radiation. Furthermore, Herb et al. (2008) report that a plant canopy affects both the surface heat transfer and the temperature of the soil below it. In fact, the tree canopy intercepts radiation, reducing the amount of short-wave radiation that reaches the soil. Furthermore, the canopy absorbs long-wave radiation, reradiating it down. In addition, the tree shelters the ground from wind, reducing heat fluxes, and plant transpiration compliments evaporation from the soil (Herb et al., 2008). In Tokyo, the Ts was estimated to be reduced by 2 to 4°C by the presence of plant canopies (Murakami et al., 1999). However, few studies have investigated the effect of tree species on air and surface temperatures. In Basel, Switzerland, a study conducted on 13 tree species illustrated that trees with smaller leaves cool their immediate environment more than trees with larger leaves (Leuzinger et al., 2010). Therefore, these results suggest that the cooling effect of urban trees could be species specific. The aim of this study was to investigate Ts variability under the shade of different tree species and to test if Ts can be estimated by a heat transfer model. For these purposes, temperature data on pavement and grass surfaces, under different shading conditions, were collected. Then, data were used for testing the performance of a one-dimensional heat transfer model integrated with a routine for estimating the effect of plant canopies on surface heat transfer. Finally, the model was tested on two surfaces—asphalt and grass—with different types of shade trees to study their effects on the ground Ts.

Materials and Methods Study Site

The case study was performed in Florence, Italy, which is characterized by a sub-Mediterranean climate with warm to hot,

dry summers and mild, wet winters. Data were collected in the Cascine park and its surroundings. This area is characterized by a high amount of vegetation, low-rise school and institutional buildings with courtyards and mature trees, street trees, asphalt streets, and car parks (Fig. 1). The types of ground materials assessed for this study were nonporous asphalt and irrigated grass. These ground materials were characterized by different hydrological, thermal, and radiative properties (Fig. 1). Material emissivity (es), heat capacity (C; J m-2 K−1), and thermal conductivity (k, kW m−1 K−1) were set according to Oke (1987), Campbell and Norman (1989), and Yaghoobian et al. (2010). Albedo (a) was measured with an albedometer (Netsens). The saturated hydraulic conductivity (Ksat), the upper limit soil water content (UL), and the field capacity water content (FC) were set according to the geological map of the area (LaMMA, 2012). Surface temperature under different shading conditions was measured, modeled, and tested on 10 tree species commonly used in Florentine and Italian urban settings (Fig. 1): Acer negundo L. (ACE), Aesculus hippocastanum L. (AES), Cedrus deodara G.Don. (CED), Celtis australis L. (CEL), Ligustrum lucidum W.T. Aiton (LIG), Olea europaea L. (OLE), Pinus pinea L. (PIN), Platanus occidentalis L. (PLA), Prunus cerasifera Ehrh. var Pissardii (PRU), and Tilia cordata L. (TIL). For each tree species, four individuals with similar characteristics (crown diameter, height, pruning, and shading condition) and with crown clearly separated from other surrounding surfaces were selected. The measured leaf area index (LAI; m2 m−2) and the dendrometric characteristics of the selected trees are shown in Table 1. Meteorological data were collected by a station equipped with sensors for measuring air temperature (Ta) in degrees centigrade (°C) and relative humidity (Thermo-hygrometer PS-0062-AD, Netsens), solar radiation (Global radiation sensor, Netsens), and wind speed (Anemometer 6410, Davis Instruments Corp.). All sensors were located at a height of about 2 m to analyze the thermal conditions at the pedestrian level. The station was placed on

Fig. 1. Left: Aerial photo of the study area, including the surroundings of the Cascine park in Florence. Symbols are used to indicate individual trees species. Right: Ground layers profile and thermal and radiative properties of the materials used in this study. The a, albedo; C, heat capacity (J m−2 K−1); es, emissivity; FC, field capacity water content; k, thermal conductivity (kW m−1 K−1); Ksat, saturated hydraulic conductivity (mm h−1); UL, upper limit soil moisture. Journal of Environmental Quality 147

Table 1. Measured dendrometric information of selected trees. Tree species

Tree species abbrev.

Acer negundo L. (box elder) Aesculus hippocastanum L. (horse-chestnut) Cedrus deodara G.Don. (Deodar cedar) Celtis australis L. (nattle tree) Pinus pinea L. (Italian stone pine) Platanus occidentalis L. (American sycamore) Prunus cerasifera Ehrh. var Pissardii (purple leaved cherry plum) Olea europaea L. (olive) Ligustrum lucidum W.T. Aiton (broad-leaf privet) Tilia x europea L. (European common lime)

ACE AES CED CEL PIN PLA

Crown average diameter

Total height

———— m ———— 4 ± 0.2† 2.6 ± 0.2 6.6 ± 0.4 8.1 ± 0.2 9.2 ± 1.2 14.1 ± 0.7 8.7 ± 0.8 8.2 ± 0.4 16.6 ± 0.7 15.4 ± 2.7 17.2 ± 1.5 17.2 ± 4.1

Leaf area index m2 m−2 4.3 ± 0.05 3.8 ± 0.1 5.5 ± 0.05 2.8 ± 0.2 2.9 ± 0.5 4.1 ± 0.4

PRU

4.5 ± 0.2

2.7 ± 0.1

4.7 ± 0.5

OLE LIG TIL

3.5 ± 0.2 3.8 ± 0.3 13.3 ± 0.7

2.3 ± 0.3 3.9 ± 0.5 8.5 ± 1.4

3.1 ± 0.1 3.4 ± 0.3 8.4 ± 0.4

† Values are mean ± SD.

the grass more than 10 m away from buildings or other standing obstacles (Fig. 1) to obtain representative measurements in builtup areas (Oke, 2006). Soil temperature sensors (TMC20-HD, Onset Comp.) were used to measure Ts on asphalt and grass exposed to the sun (SUN) and under the shade of an A. negundo, a tree commonly used in streets and parks of Florence. Soil temperature sensors were buried horizontally 10 mm deep. This network operated continuously from 1 June 2014 to the end of August 2014 (Brandani et al., 2015). For each surface and exposition, Ts at noon was measured by an infrared thermometer (IR) (Agri-Therm II, Everest Intersciences) as the mean of five measurements collected between 12:00 and 13:00. Surface temperature by IR was collected for 18 d (sunny without wind) during the study period. Sun-exposed Ts by IR was measured far away from the tree crowns, and shaded Ts by IR was measured in the middle of the tree crown shade. A LAI meter (LAI-2000 Plant Canopy Analyzer, LI-COR) was used to determine the LAI of the trees.

Thermal and Water Fluxes in the Soil Profile A simplified, unsteady, and one-dimensional heat transfer in a soil column model was developed to simulate Ts for pavement/vegetated surfaces with an hourly temporal resolution. The model uses up to 10 layers of different thickness (from 0.01 to 10 m) and materials. The unsteady, one-dimensional heat conduction equation was discretized as reported by Herb et al. (2008): Ti j +1 =

æ Di-1, i j ö D Dz Ti-1 + i , i +1 Ti +j 1 + i Ti j ÷÷÷ ççç çè dz dz Dt ø÷ i-1

i

æ Di-1, i Di , i +1 Dzi ö÷ çç ÷ + + çèç dz dz i Dt ÷ø÷ i-1

[1]

where T is temperature (°C); D is the thermal diffusivity; ∆t is the time step; ∆z is the layer thickness; i and j are indices for depth and time discretization, respectively; and dz is the node spacing. The value of D was assumed to be constant within each layer, whereas at the interface of two layers D was calculated using a harmonic mean as reported by Patankar (1980). A specified temperature at the lower soil boundary was assumed to be a function of time of year, the depth below the surface, and the mean annual Ta as proposed by Kasuda and Archenbach (1965). 148

For the application of the model to pervious land surfaces, the soil moisture transport between layers was modeled as proposed by Savabi and Williams (1995). The infiltration rate (fi) (m s-1) was calculated using the Green–Ampt formulation (Green and Ampt, 1911). If precipitation rate exceeded fi or if the computed infiltration volume exceeded the upper limit of water storage, the remaining water was assumed to be removed from the system by runoff at the end of each time step. Percolation (pe) of water in excess of field capacity (FC) from a layer was computed as follows: é æ -Dt ö÷ù ÷ú Qi > FCi [2] pe i = (Qi - FCi ) êê1- expççç çè t i ø÷÷úú ëê û pei = 0 Qi ≤ FCi [3] where Qi and FCi are the actual and the field capacity water content for layer i (m), respectively, and t is travel time through the layer i (s). Moreover, pei was restricted by a lower layer, which was at or near saturation, as follows:

pe i = pe i 1-

Qi +1 [4] UL i +1

where Qi+1 and ULi+1 are the actual and the upper limit soil moisture of the lower layer. The value of t was computed with a linear storage equation (Savabi and Williams, 1995): ti = (Qi - FCi)KSi-1 [5] where average hydraulic conductivity (KSi) (m s-1) in each layer was estimated as follows:

(

K Si = K SATi Qi UL i-1

)

Bi

[6]

é æ FC öù -1 Bi = - 2.655 êê log ççç i ÷÷÷úú [7] ÷ ç ëê è UL i øûú

where KSATi is the saturated hydraulic conductivity for layer i (m s-1), and ULi is the upper limit soil water content for layer i (m).

Journal of Environmental Quality

Energetic Balance The energy balance for a flat surface can be written as: (1 - a)(Rsdr + Rsdf ) + (Rli - Rlo) - HL - HS = Hnet [8] where Rsdr and Rsdf are the flux densities of direct and diffuse shortwave radiation, respectively; Rli and Rlo are the downwelling and outcoming long-wave radiative flux densities, respectively; HS and HL are the sensible heat flux and the latent heat flux, respectively, from the surface; and Hnet is the total net surface heat transfer. Positive and negative signs stand for the incoming and outgoing fluxes, respectively. The method developed by Liu and Jordan (1960) was used to compute the Rsdr and Rsdf (Napoli et al., 2014). The method is based on the assumption that the ratio of Rsdf on the horizontal plane and the incoming solar radiation (Rs) is a function of a clearness index (Kt) according to the formula: Rsdf = (1.39 - 4.072Kt + 5.531Kt2 - 3.108Kt3)Rs [9] Rsdr = Rs - Rsdf [10] Kt is defined, for a given geographic location, as the ratio between the Rs and the extraterrestrial radiation (Ra) on the horizontal plane (Liu and Jordan, 1960). Hourly Ra on a horizontal plan was estimated as reported in Allen et al. (1998):

Ra =

12·60 GSC dr [(w2 -w1 )(sin s sin j ) p [11] + {cos s cos j éë sin (w2 )- sin (w1 )ùû }]

where Gsc is the solar constant (3416.67 J m-2 h-1); dr is the inverse relative distance from the Earth to the Sun; s is the solar declination; w1 and w2 are, respectively, the solar time angle at the beginning and at the end of period (rad); and j is the latitude (rad). The dr is calculated as reported in ASCE (1996), and s is calculated using the formula from Perrin de Brichambaut (1975). The other parameters were computed as reported in Allen et al. (1998). The canopy transmittance (ti) was estimated by applying the Beer–Lambert law (Monsi and Saeki, 1953) as reported by Hellström (2000): ti = exp(-kLAI) [12] where k is a vegetation dependent attenuation coefficient assuming a spherical leaf angle distribution as suggested by Tarboton and Luce (1996): k = (2cosq)-1 [13] where q is the solar zenith angle as reported in Campbell and Norman (1989). The Rli is based on the formula given by Fischer et al. (1979) and Henderson-Sellers (1986): Rli = eas(Ta + 273.15)4(1 + 0.17Cf2)(1 - a) [14] where ea is the air emissivity calculated according to Swinbank (1963), s is the Stefan-Boltzmann constant, and Cf is the fractional cloud cover.

The Rlo at soil Ts was calculated following the StefanBoltzmann equation as: Rlo = ess(Ts + 273.15)4 [15] The HL was directly coupled to the actual evapotranspiration (ETa) (mm h-1) as follows (Allen et al., 1998): HL = lETa [16] where l is the latent heat of vaporization (MJ kg-1) computed as follows: l = 2.501 - 2.361(103)Ta [17] The ETa was calculated as function of the potential soil evaporation (Esp) (mm h−1), the plant transpiration (Etp) (mm h−1) (Savabi and Williams, 1995): ETa = f(Esp, Etp, Kc) [18] Esp = EToexp-0.4LAI [19]

æ Esp ö÷ ÷ ET [20] Etp = ççç1çè ETo ÷÷ø o where ETo is the reference evapotranspiration, and Kc is the single crop coefficient as reported in Allen et al. (1998). The terms Esp and Etp were further adjusted as a function of LAI and water stress factor according to Savabi and Williams (1995). For paved surfaces, the Esp was calculated only when standing water was present, whereas ETo and Etp were computed only when a tree was present. The HS was parameterized by a resistance-type formulation using the difference between the Ts and Ta measured 2 m above the surface:

HS =

rc p (Ts -Ta ) rh

[21]

where rh is the resistance to heat transfer from a surface computed as reported in Allen et al. (1998), r is the air density, and cp is the air specific heat at constant pressure.

Model Setup and Sensitivity Analysis The simulation was run from 1 June to 31 August. The initial soil water content was assumed to be equal to the soil field capacity in the profile. A 1-mo warm-up period was used to avoid the influence of the initial condition on the simulation results. No model calibration was performed. The uncertainty in the model performance measured by Nash–Sutcliffe coefficient (NSC) (Nash and Sutcliffe, 1970) was performed by sensitivity analysis. Five parameters affecting three soil/vegetation properties were varied: LAI for each tree species, Ksat of asphalt (Ksat A) and pebble (Ksat P) for asphalt surfaces and loamy soil (Ksat LS) for grass surfaces, and Kc for evapotranspiration from both asphalt and grass surfaces. The NSC was evaluated for five different values in the range of each parameter (the initial value and, respectively, the initial value +25%, +50% and −25%, −50% of the range). The LAI range was determined by minimum and maximum LAI measured for each species at the site; Ksat and Kc ranges were found in the published literature.

Journal of Environmental Quality 149

Sensitivity analysis was conducted both on sensor data and IR data. Model error was calculated as the hourly difference between actual Ts and estimated Ts on the four surface sensors in July and August 2014 (sun-exposed asphalt and grass, asphalt and grass under the shade of ACE). Model performances during nighttime (20:00–07:00) and daytime (08:00–19:00) were also evaluated. Hourly error distribution was presented using a boxplot (100%, 75%; average, 25%, 0%). The 95th percentile confidence interval of residuals analysis (CIres) was determined. Model performance was tested using statistics and criteria recommended by Moriasi et al. (2007). Three statistical criteria were used to evaluate the goodness of fit of the model: the mean percentage error (M%E), which measures model overestimation or underestimation of measured values (Mayer and Butler, 1993), the ratio of the root mean square error to observation standard deviation (RSR) (Moriasi et al., 2007), and NSC. The model evaluation guidelines for the systematic quantification of accuracy (very good, good, satisfactory, and unsatisfactory) by Moriasi et al. (2007) were applied throughout the study. The Ts of asphalt and grass measured by IR outside and under the trees canopy of different species were compared by means of ANOVA, and pairwise comparisons were performed by means of the Bonferroni test. Then, the model performance was tested on these data by using the same approach and statistical indices described before.

Results

Meteorological Conditions during the Study Period The meteorological conditions during the study period were compared with the corresponding long-term values (standard period, 1961–1990) recorded at the Firenze-Ximeniano weather station, which is situated at a distance of 2.9 km from the study area. During the study period, Ta and Rs followed long-term climate averages (Ta = 23.3°C; Rs = 21.8 MJ m−2), whereas rainfall data showed higher values during July (rainfall, 76.4 mm; n = 13 rainy days) and lower values during August (rainfall, 27 mm; n = 5 rainy days). In particular, during July, the average rainfall together with the number of rainy days exceeded the long-term means by about 50.2 mm and 10 d, respectively.

Measured Asphalt and Grass Surface Temperature below Different Types of Trees in the Middle of the Day The Ts on asphalt exposed to the sun (Ts = 53.8°C) was significantly higher than the Ts under the tree crowns (Ts 0.75) in estimating hourly temperature sensor data for each surface in both radiation conditions (i.e., exposed to the sun and under the tree shade), (ii) model performance in estimating IR data on asphalt and grass surfaces was very good for most of the tree species (good for TIL on both exposures, sufficient for PIN and LIG on exposed surfaces, and for ACE and CED in shade conditions), and (iii) asphalt surface temperature was negatively correlated to LAI (r = −0.79; p < 0.01), whereas no significant relationship was found between grass Ts and LAI. The lower Ts recorded under the tree canopy confirmed the theory of the benefit of urban trees in reducing the heat stored in engineered surfaces (Armson et al., 2013; Cantón et al., 1994; Heisler, 1986). Furthermore, the negative relationship found between asphalt Ts and LAI is consistent with similar results found in United Kingdom by Armson et al. (2013), who

reported that tree species with higher LAI provided significantly more cooling than those with lower LAI. Even though there are several models that simulate thermal conditions within an urban environment, most of them are not specifically focused on Ts prediction. Therefore, it is very difficult to compare models. Herb et al. (2008) investigated temperature on paved and vegetation-covered surfaces. Their model was able to predict hourly Ts of bare soil and pavement, with RMSEs ranging from 1 to 2°C and hourly Ts of vegetation-covered surfaces with RMSEs ranging from 1 to 3°C. Yang et al. (2013) estimated Ts by using the ENVI-met model in a study site characterized by ceramic tile, concrete, asphalt, and grass surfaces. They assessed model performance for all the surfaces, and the average RMSE was 1.98°C. Masson et al. (2002) evaluated the Town Energy Balance model performance on direct measurements from dry districts in two cities and found an average RMSE of 4°C on road surfaces. Our model was able to predict hourly Ts on sun-exposed and shaded asphalt with RMSEs ranging from 0.46 to 0.66°C and with hourly Ts of sun-exposed and shaded grass with RMSEs

Table 3. Stats of the comparison between the data estimated by the model with respect to the measured data in sun-exposed conditions and under different tree covers. Surface

Asphalt

Grass

Tree cover

temperature in the sun Acer negundo L. Celtis australis L. Pinus pinea L. Platanus occidentalis L. Ligustrum lucidum W.T. Aiton Tilia cordata L. temperature in the sun Acer negundo L. Aesculus hippocastanum L. Cedrus deodara G.Don Olea europaea L. Pinus pinea L. Prunus cerasifera Ehrh. var Pissardii Tilia cordata L.

Mean observed temp.

M%E

Statistics† RSR

NSC

°C 53.94 32.78 40.26 39.76 36.14 39.22 31.02 40.67 29.66 32.72 30.18 32.52 30.35 30.88 31

−1.70%a‡ 2.80%a −0.20%a −1.90%a −0.60%a −2.60%a 3.30%a 0.40%a 1.70%a −1.90%a 2.50%a −0.20%a 0.40%a 0.80%a −0.30%a

0.413a 0.466a 0.477a 0.658c 0.439a 0.603c 0.587b 0.401a 0.615c 0.359a 0.605c 0.349a 0.314a 0.406a 0.556b

0.821a 0.78a 0.759a 0.567c 0.796a 0.616c 0.675b 0.832a 0.629c 0.864a 0.645c 0.871a 0.895a 0.827a 0.673b

† M%E, mean percentage error; NSC, Nash–Sutcliffe coefficient; RSR, ratio of the root mean square error to the standard deviation of measured data. ‡ The letters a, b, and c indicate that the statistic result is very good, good, and satisfactory, respectively (Moriasi et al., 2007). 152

Journal of Environmental Quality

Table 4. Nash–Sutcliffe coefficient sensitivity of the model applied to infrared data to variation of leaf area index of each species and single crop coefficient on asphalt and grass and saturated hydraulic conductivity of asphalt, pebble, and loamy soil. Parameter† LAI

 Asphalt

 Grass

Kc

 Asphalt

 Grass

Ksat A  Asphalt

Ksat P  Asphalt

Ksat LS  Grass

Tree species§ ACE AES CED CEL PIN PLA PRU LIG OLE TIL mean 4.30 2.80 2.90 4.10 3.40 8.40 range 4.21–4.35 2.51–2.93 2.21–3.48 3.65–4.67 2.85–3.72 7.87–9.20 NSC(1)¶ 0.772 0.690 0.473 0.762 0.675 0.665 NSC(2) 0.775 0.739 0.725 0.796 0.673 0.672 NSC(3) 0.777 0.760 0.580 0.793 0.641 0.677 NSC(4) 0.780 0.757 0.576 0.767 0.590 0.682 NSC(5) 0.782 0.733 0.419 0.725 0.527 0.685 mean 4.30 3.80 5.50 2.90 4.70 3.10 8.40 range 4.21–4.35 3.69–3.95 5.45–5.55 2.21–3.48 4.22–5.35 2.97–3.22 7.87–9.20 NSC(1) 0.617 0.881 0.642 0.856 0.806 0.871 0.678 NSC(2) 0.621 0.874 0.646 0.889 0.821 0.872 0.675 NSC(3) 0.625 0.867 0.648 0.896 0.829 0.871 0.672 NSC(4) 0.629 0.859 0.649 0.887 0.834 0.870 0.670 NSC(5) 0.632 0.851 0.649 0.871 0.837 0.867 0.668 mean 0.20 1.10 range 0.10–0.30 0.82–1.38 NSC(1) 0.845 0.774 0.740 0.601 0.801 0.660 0.646 NSC(2) 0.838 0.778 0.759 0.587 0.803 0.641 0.661 NSC(3) 0.821 0.780 0.759 0.567 0.796 0.616 0.675 NSC(4) 0.794 0.779 0.741 0.539 0.779 0.583 0.687 NSC(5) 0.758 0.777 0.702 0.506 0.753 0.544 0.698 mean 0.75 1.10 range 0.56–0.94 0.82–1.38 NSC(1) 0.242 0.275 0.825 0.410 −0.35 0.587 0.264 0.476 NSC(2) 0.649 0.457 0.897 0.500 0.516 0.729 0.693 0.591 NSC(3) 0.832 0.629 0.864 0.645 0.895 0.827 0.871 0.673 NSC(4) 0.763 0.681 0.710 0.709 0.670 0.828 0.759 0.702 NSC(5) 0.413 0.549 0.419 0.691 −0.29 0.716 0.310 0.671 SUN‡

mean range NSC(1) NSC(2) NSC(3) NSC(4) NSC(5) mean range NSC(1) NSC(2) NSC(3) NSC(4) NSC(5) mean range NSC(1) NSC(2) NSC(3) NSC(4) NSC(5)

0.780 0.780 0.780 0.780 0.780

0.780 0.780 0.780 0.780 0.780

0.515 0.572 0.629 0.631 0.524

0.587 0.825 0.864 0.782 0.618

0.759 0.759 0.759 0.759 0.759

0.20 0–0.40 0.567 0.796 0.567 0.796 0.567 0.796 0.567 0.796 0.567 0.796

0.616 0.616 0.616 0.616 0.616

0.675 0.675 0.675 0.675 0.675

0.759 0.759 0.759 0.759 0.759

50 30–70 0.567 0.796 0.567 0.796 0.567 0.796 0.567 0.796 0.567 0.796

0.616 0.616 0.616 0.616 0.616

0.675 0.675 0.675 0.675 0.675

0.501 0.612 0.645 0.632 0.606

2.00 1.50–2.50 0.367 0.675 0.871 0.583 0.241

0.477 0.769 0.895 0.899 0.498

0.656 0.773 0.827 0.756 0.516

0.551 0.614 0.673 0.672 0.643

† Kc, single crop coefficient; Ksat, saturated hydraulic conductivity (A, asphalt; LS, loamy soil; P, pebble); LAI, leaf area index. ‡ Asphalt and grass exposed to the sun. § ACE, Acer negundo L.; AES, Aesculus hippocastanum L.; CED, Cedrus deodara G.Don.; CEL, Celtis australis L.; LIG, Ligustrum lucidum W.T. Aiton; OLE, Olea europaea L.; PIN, Pinus pinea L.; PLA, Platanus occidentalis L.; PRU, Prunus cerasifera Ehrh. var Pissardii; TIL, Tilia cordata L. ¶ 1: Mean, 50% range. 2: Mean, 25% range. 3: Mean. 4: +25% range; mean, +50% range.

Journal of Environmental Quality 153

Table 5. Nash–Sutcliffe coefficient sensitivity of the model applied to hourly sensor data to variation of leaf area index of each species and single crop coefficient on asphalt and grass, and saturated hydraulic conductivity of asphalt, pebble, and loamy soil. The analysis was performed during the whole day, day, and night. Parameter† LAI

 Asphalt

 Grass

Kc

 Asphalt

 Grass

Ksat A  Asphalt

Ksat P  Asphalt

Ksat LS  Grass

Whole day

Sun Day

Night

mean range NSC(1)‡ NSC(2) NSC(3) NSC(4) NSC(5) mean min–max NSC(1) NSC(2) NSC(3) NSC(4) NSC(5)

Whole day

Shade Day

Night

4.30 4.21–4.35 0.997 0.997 0.997 0.997 0.997 4.3 4.21–4.35

4.30 4.21–4.35 0.997 0.997 0.997 0.997 0.996 4.3 4.21–4.35

4.30 4.21–4.35 0.976 0.976 0.976 0.976 0.976 4.3 4.21–4.35

0.981 0.981 0.981 0.981 0.982

0.956 0.957 0.958 0.959 0.960

0.875 0.875 0.876 0.876 0.876

mean range NSC(1) NSC(2) NSC(3) NSC(4) NSC(5) mean range NSC(1) NSC(2) NSC(3) NSC(4) NSC(5)

0.20 0.10–0.30 0.993 0.994 0.994 0.994 0.992 0.75 0.56–0.94 0.968 0.988 0.995 0.988 0.964

0.20 0.10–0.30 0.996 0.997 0.997 0.997 0.996 0.75 0.56–0.94 0.953 0.985 0.997 0.985 0.947

0.20 0.10–0.30 0.964 0.964 0.964 0.964 0.964 0.75 0.56–0.94 0.967 0.967 0.967 0.967 0.967

1.10 0.82–1.38 0.997 0.997 0.997 0.997 0.996 1.1 0.82–1.38 0.975 0.980 0.981 0.978 0.969

1.10 0.82–1.38 0.997 0.997 0.997 0.996 0.993 1.1 0.82–1.38 0.931 0.953 0.958 0.944 0.906

1.10 0.82–1.38 0.976 0.976 0.976 0.976 0.976 1.1 0.82–1.38 0.876 0.876 0.876 0.876 0.876

mean range NSC(1) NSC(2) NSC(3) NSC(4) NSC(5)

0.20 0–0.40 0.972 0.988 0.994 0.928 0.727

0.20 0–0.40 0.978 0.992 0.997 0.939 0.762

0.20 0–0.40 0.963 0.963 0.964 0.963 0.963

0.20 0–0.40 0.995 0.997 0.997 0.996 0.993

0.20 0–0.40 0.993 0.996 0.997 0.993 0.986

0.20 0–0.40 0.973 0.974 0.976 0.975 0.973

mean range NSC(1) NSC(2) NSC(3) NSC(4) NSC(5)

50 30–70 0.823 0.951 0.994 0.433 0.275

50 30–70 0.970 0.991 0.997 0.985 0.912

50 30–70

50 30–70 0.889 0.968 0.997 0.892 0.559

50 30–70

-0.195 -5.779

50 30–70 0.790 0.941 0.997 0.637 0.452

-1.251 -2.259

mean range NSC(1) NSC(2) NSC(3) NSC(4) NSC(5)

2 1.50–2.50 0.894 0.915 0.995 0.844 0.810

2 1.50–2.50 0.837 0.869 0.997 0.756 0.702

2 1.50–2.50 0.963 0.966 0.967 0.966 0.964

2 1.50–2.50 0.952 0.973 0.981 0.966 0.942

2 1.50–2.50 0.844 0.928 0.958 0.899 0.808

2 1.50–2.50 0.830 0.852 0.876 0.856 0.812

-0.941 0.466 0.964

-1.264 0.373 0.976

† Kc, single crop coefficient; Ksat, saturated hydraulic conductivity (A, asphalt; LS, loamy soil; P, pebble); LAI, leaf area index. ‡ 1: Mean, 50% range. 2: Mean, 25% range. 3: Mean. 4: +25% range; mean, +50% range. 154

Journal of Environmental Quality

ranging from 0.58 to 1.15°C. These results are important if we consider that Ts was estimated by a combination of models (energetic balance, thermal, and water fluxes in the soil profile) and when using Ta, relative humidity, and Rs from a weather station as input data. Model parameterization has been done by using bibliographic data and applying the model without any calibration. Sensitivity analysis showed that the model performance on grass surfaces was very sensitive to Kc and Ksat LS. Model sensitivity to Kc depends on the species, suggesting that a fixed value of Kc is not sufficient to model water content dynamics due to evapotranspiration. Model sensitivity to Ksat LS depends on soil moisture, especially during rainy days. These results underlined that soil moisture has an important impact on grass Ts. Further model improvements could be achieved by investigating speciesspecific Kc coefficients. The model also showed a slight sensitivity to LAI for tree species with LAI 3, as indicated by Savabi and Williams (1995). Generally, sensitivity was lower on sensor data than IR measurements. Low sensitivity on sensor continuous data indicates a good model reliability, suggesting that it can be effectively applied in future applications. The model applied to measurements taken by IR appeared to slightly underestimate Ts on asphalt and to slightly overestimate Ts on grass. A source of error could be the difference between the skin temperature measured by the IR and the temperature of the surface layer provided by the model, which has 0.05 and 0.1 m thickness for grass and asphalt, respectively. Future model assessments need to be performed to test the model on other ground surfaces and under other tree shade conditions. Future studies will also include field measurements using a globe thermometer to test if this model could be used to estimate the mean radiant temperature, one of the most important parameters to predict outdoor human thermal comfort (Huang et al., 2014; Folk, 1974).

Conclusions In this study, surface temperature on asphalt and grass under different tree species was analyzed and modeled. To our knowledge, this was the first time that such an investigation was performed taking into consideration 10 tree species commonly used in an urban environment. On asphalt, trees with higher LAI reduced Ts significantly because of the lower amount of Rs that reached the ground; on grass, this relationship was weaker probably because of the combined effect of shade and grass evapotranspiration on Ts. The proposed model was a combination of relatively simple soil heat conduction equations. Nevertheless, according to our analysis the model provided very good predictions of the hourly Ts for grass and asphalt with different types of shade trees without any prior calibration. Therefore, despite the limited period analyzed, the results from this model are encouraging. Sensitivity analysis confirmed that parameters affecting soil moisture play an important role in modeling Ts on grass surfaces, whereas on asphalt Ts is significantly related to tree shade (LAI). Low sensitivity on sensor continuous data indicates a good model reliability, suggesting that it can be effectively applied in future applications. Our findings underline the importance of investigating species-specific mitigation effects on urban surfaces.

References Abreu, L.V., L.L. Labaki, and A. Matzarakis. 2012. Bioclimate provide by shade trees as factor in urban and architectural planning in tropical climates: The case of Campinas, Brazil. 4th CIB International Conference on Smart and Sustainable Built Environments, São Paulo, Brazil. Allen, R.G., L.S. Pereira, D. Raes, and M. Smith. 1998. Crop evapotranspiration: Guidelines for computing crop requirements. Irrigation and Drainage Paper 56. FAO, Rome. American Society of Civil Engineers (ASCE). 1996. Hydrology handbook. 2nd ed. ASCE manuals and reports on engineering practice No. 28. ASCE, Reston, VA. Armson, D., M.A. Rahman, and A.R. Ennos. 2013. A comparison of the shading effectiveness of five different street tree species in Manchester, UK. Arboriculture Urban Forestry 39:157–164. Arnfield, A.J. 2003. Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island. Int. J. Climatol. 23:1–26. doi:10.1002/joc.859 Asaeda T, T.C. Vu, and A. Wake. 1996. Heat storage of pavement and its effect on the lower atmosphere. Atmosph. Environ. 30(3):413–427. Brandani, G., M. Napoli, L. Massetti, M. Petralli, and S. Orlandini. 2015. Urban soil: Assessing the impact of ground cover on air temperature, surface temperature and thermal comfort. J. Environ. Qual. doi:10.2134/ jeq2014.12.0521 Ca, V.T., T. Asaeda, and E.M. Abu. 1998. Reductions in air conditioning energy caused by a nearby park. Energy Build. 29:83–92. doi:10.1016/ S0378-7788(98)00032-2 Campbell, G.S., and J.M. Norman. 1989. Plant physiological ecology field methods and instrumentation. In: G. Russell, B. Marshall, and P.G. Jarvis, editors, Canopy structure. Chapman and Hall, London. p. 301–325. Cantón, M.A., J.L. Cortegoso, and C. de Rosa. 1994. Solar permeability of urban trees in cities of western Argentina. Energy Build. 20:219–230. doi:10.1016/0378-7788(94)90025-6 Doulos, L., M. Santamouris, and I. Livada. 2004. Passive cooling of outdoor urban spaces: The role of materials. Sol. Energy 77:231–249. doi:10.1016/j. solener.2004.04.005 Fischer, H.B., E.J. List, R.C.Y. Koh, J. Imberger, and N.H. Brooks. 1979. Mixing in inland and coastal waters. Academic Press, San Diego, CA. Folk, G. 1974. Textbook of environmental physiology. Lea & Febiger, Philadelphia, PA. Green, W.H., and G.A. Ampt. 1911. Studies in soil physics: I. The flow of air and water through soils. J. Agric. Sci. 4:1–24. doi:10.1017/ S0021859600001441 Grimmond, C.S.B., and T.R. Oke. 1991. An evaporation-interception model for urban areas. Water Resour. Res. 27:1739–1755. doi:10.1029/91WR00557 Grimmond, C.S.B., M. Best, J. Barlow, A.J. Arnfield, J.-J. Baik, S. Belcher, M. Bruse, I. Calmet, F. Chen, P. Clark, A. Dandou, E. Erell, K. Fortuniak, R. Hamdi, M. Kanda, T. Kawai, H. Kondo, S. Krayenhoff, S.H. Lee, S.-B. Limor, A. Martilli, V. Masson, S. Miao, G. Mills, R. Moriwaki, K. Oleson, A. Porson, U. Sievers, M. Tombrou, J. Voogt, and T. Williamson. 2009. Urban surface energy balance models: Model characteristics and methodology for a comparison study. In: A. Baklanov, G. Sue, A. Mahura, and M. Athanassiadou, editors, Meteorological and air quality models for urban areas. Springer, Berlin. p. 97–123. Heisler, G.M. 1986. Effects of individual trees on the solar radiation climate of small buildings. Urban Ecol. 9:337–359. doi:10.1016/0304-4009(86)90008-2 Hellström, R.Å. 2000. Forest cover algorithms for estimating meteorological forcing in a numerical snow model. Hydrol. Processes 14:3239– 3256. doi:10.1002/1099-1085(20001230)14:183.0.CO;2-O Henderson-Sellers, B. 1986. Calculating the surface energy balance for lake and reservoir modelling: A review. Rev. Geophys. 24:625–649. doi:10.1029/ RG024i003p00625 Herb, W., B. Janke, and O. Mohseni. 2008. Ground surface temperature simulation for different land covers. J. Hydrol. 356:327–342. doi:10.1016/j. jhydrol.2008.04.020 Huang, J., J.C. Cedeño-Laurent, and J.D. Spengler. 2014. CityComfort+: A simulation-based method for predicting mean radiant temperature in dense urban areas. Build. Environ. 80:84–95. doi:10.1016/j.buildenv.2014.05.019 Kasuda, T., and P.R. Archenbach. 1965. Earth temperature and thermal diffusivity at selected stations in the United States. ASHRAE Trans. 71:965. Krayenhoff, E.S., A. Christen, A. Martilli, and T.R. Oke. 2014. A multi-layer radiation model for urban neighbourhoods with trees. Boundary-Layer Meteorol. 151:139–178. doi:10.1007/s10546-013-9883-1 LaMMA. 2012. 10K Regional geological map. www.lamma.rete.toscana.it/ en/10k-regional-geological-map (accessed 7 Mar. 2015).

Journal of Environmental Quality 155

Lemonsu, A., V. Masson, L. Shashua-Bar, E. Erell, and D. Pearlmutter. 2012. Inclusion of vegetation in the Town Energy Balance model for modelling urban green areas. Geosci. Model Dev. 5:1377–1393. doi:10.5194/ gmd-5-1377-2012 Leuzinger, S., R. Vogt, and C. Körner. 2010. Tree surface temperature in an urban environment. Agric. For. Meteorol. 150:56–62. doi:10.1016/j. agrformet.2009.08.006 Lindberg, F., and C.S.B. Grimmond. 2011. Nature of vegetation and building morphology characteristics across a city: Influence on shadow patterns and mean radiant temperatures in London. Urban Ecosyst. 14:617–634. doi:10.1007/s11252-011-0184-5 Liu, B.Y.H., and R.C. Jordan. 1960. The interrelationship and characteristic distribution of direct, diffuse and total solar radiation. Sol. Energy 4:1–19. doi:10.1016/0038-092X(60)90062-1 Masson, V. 2000. A physically-based scheme for the urban energy budget in atmospheric models. Boundary-Layer Meteorol. 94:357–397. doi:10.1023/A:1002463829265 Masson, V., C.S.B. Grimmond, and T.R. Oke. 2002. Evaluation of the town energy balance (TEB) scheme with direct measurements from dry districts in two cities. J. Appl. Meteorol. 41:1011–1026. doi:10.1175/1520-0450(2002)0412.0.CO;2 Mayer, D.G., and D.G. Butler. 1993. Statistical validation. Ecol. Modell. 68:21– 32. doi:10.1016/0304-3800(93)90105-2 Middel, A., K. Häb, A.J. Brazel, C.A. Martin, and S. Guhathakurt. 2014. Impact of urban form and design on mid-afternoon microclimate in Phoenix local climate zones. Landsc. Urban Plan. 122:16–28. doi:10.1016/j. landurbplan.2013.11.004 Monsi, M., and T. Saeki. 1953. Über den lichtfaktor in den pflanzengesellschaften und seine bedeutung für die stoffproduktion. Jpn. J. Bot. 14:22–52. Moriasi, D.N., J.G. Arnold, M.W. Van Liew, R.L. Bingner, R.D. Harmel, and T.L. Veith. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50:885–900. doi:10.13031/2013.23153 Murakami, S., R. Ooka, A. Mochida, S. Yoshida, and S. Kim. 1999. CFD analysis of wind climate from human to urban scale. J. Wind Eng. Ind. Aerodyn. 81:57–81. doi:10.1016/S0167-6105(99)00009-4 Napoli, M., S. Cecchi, S. Orlandini, and C.A. Zanchi. 2014. Determining potential rainwater harvesting sites using a continuous runoff potential accounting procedure and GIS techniques in central Italy. Agric. Water Manage. 141:55–65. doi:10.1016/j.agwat.2014.04.012

156

Nash, J.E., and J.V. Sutcliffe. 1970. River flow forecasting through conceptual models: Part I. A discussion of principles. J. Hydrol. 10:282–290. doi:10.1016/0022-1694(70)90255-6 Oke, T.R. 1987. Boundary layer climates. Methuen & Co., York, UK. Oke, T.R. 2006. Initial guidance to obtain representative meteorological observations at urban sites. IOM Report No. 81, WMO/TD. No. 1250. World Meteorological Organization, Geneva, Switzerland. Patankar, S.V. 1980. Numerical heat transfer and fluid flow. McGraw-Hill, New York. Perrin de Brichambaut, C. 1975. Estimation des resources energetiques solaires en France. Cahiers A.F.E.D.E.S. Supplement au No. 1. Petralli, M., L. Massetti, G. Brandani, and S. Orlandini. 2014. Urban planning indicators: Useful tools to measure the effect of urbanization and vegetation on summer air temperatures. Int. J. Climatol. 34(4):1236–1244. doi:10.1002/joc.3760 Savabi, M.R., and M.R. Williams. 1995. USDA water erosion prediction project hillslope profile and watershed model documentation. NSERL Report 10. USDA–ARS National Soil Erosion Research Laboratory, West Lafayette, IN. Shaffer, S.R., W.T.L. Chow, M. Georgescu, P. Hyde, G.D. Jenerette, A. Mahalov, M. Moustaoui, and B.L. Ruddell. 2015. Multi-scale modeling and evaluation of urban surface energy balance in the Phoenix metropolitan area. J. Appl. Meteorol. Climatol. 54:322–338. doi:10.1175/JAMC-D-14-0051.1 Simpson, J.R. 2002. Improved estimates of tree-shade effects on residential energy use. Energy Build. 34:1067–1076. doi:10.1016/S0378-7788(02)00028-2 Swinbank, W.C. 1963. Long-wave radiation from clear skies. Q. J. R. Meteorol. Soc. 89:339–348. doi:10.1002/qj.49708938105 Tarboton, D.G., and C.H. Luce. 1996. Utah Energy Balance Snow Accumulation and Melt Model (UEB).Utah Water Research Laboratory, Utah State University, Logan. Yaghoobian, N., J. Kleissl, and E.S. Krayenhoff. 2010. Modeling the thermal effects of artificial turf on the urban environment. J. Clim. Appl. Meteorol. 49:332–345. doi:10.1175/2009JAMC2198.1 Yang, X., L. Zhao, M. Bruse, and Q. Meng. 2013. Evaluation of a microclimate model for predicting the thermal behaviour of different ground surfaces. Build. Environ. 60:93–104. doi:10.1016/j.buildenv.2012.11.008

Journal of Environmental Quality