Green Energy and Sustainability ISSN 2771-1641
Green Energy and Sustainability 2021;1(1):0003 | https://doi.org/10.47248/HKOD902101010003
Original Research Open AccessEvaluation of nature-based solutions implementation scenarios, using urban surface modelling
Correspondence: Dionysia Kolokotsa
Academic Editor(s): George Papadakis
Received: Apr 13, 2021 | Accepted: Jun 22, 2021 | Published: Sep 23, 2021
© 2021 by the author(s). This is an Open Access article distributed under the terms of the Creative Commons License Attribution 4.0 International (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is correctly credited.
Cite this article: Panagiotakis E, Kolokotsa D, Chrysoulakis N. Evaluation of nature-based solutions implementation scenarios, using urban surface modelling. Green Energy Sustain 2021; 1(1):0003. https://doi.org/10.47248/HKOD902101010003
The present paper aims to study the impact of Nature Based Solutions (NBS) on the urban environment. The Surface Urban Energy and Water balance Scheme (SUEWS) is used to quantify the impact of NBS in the city of Heraklion, Crete, Greece, a densely built urban area. Local meteorological data and data from an Eddy Covariance flux tower installed in the city center are used for the model simulation and evaluation. Five different scenarios are tested by replacing the city’s roofs and pavements with green infrastructure, i.e., trees and grass, and water bodies. The NBS impact evaluation is based on the changes of air temperature above 2m from the ground, relative humidity and energy fluxes. A decrease of the air temperature is revealed with the highest reduction (2.3%) occurring when the pavements are replaced with grass for all scenarios. The reduction of the air temperature is followed by a decrease in turbulent sensible heat flux. For almost all cases, an increase of the relative humidity is noticed, accompanied by a considerable increase of the turbulent latent heat flux. Therefore, NBS in cities change the energy balance significantly and modify the urban environment for the citizens' benefit.Keywords
Nature Based Solutions (NBS), SUEWS model, urban climate, urban energy fluxes
Due to the increase of the population in cities [1,2], the deep understanding of the driving forces that influence the urban environment is of major importance. Urbanization impacts the environment in several ways , affecting the urban climate and making cities potentially vulnerable. Studies around extreme weather conditions connect the city structure and characteristics with these conditions. Specifically, a natural hazard appears to be exacerbated from the urban form that contains factors such as urban surface cover, urban fabric, and surface structure . The type of surface cover in urban areas can increase the in-surface runoff and sensible heat flux and reduce the evaporation to the boundary layer of an urban area compared to a naturally vegetated area. The implications of the changes in urban surface cover impact human comfort, flooding, and pollutant dispersion mixing of the boundary layer. Even though such fluxes are not measured, they are very important for the local authorities' political management and decision-making on a spatial scale, whether at the neighborhood, block, or city level .
According to various studies [6–9], resilience and sustainability should be taken into account in cities ‘management in order to address the emerging challenges as well as the effects of climate on a large scale. The anthropogenic activity in urban areas changes the water and energy use considerably. Surface cover alterations in the urban environment increase the sensible heat flux and surface runoff and reduce evaporation, compared to the natural environment . This is mainly due to the materials used for the construction, lack of vegetation, and the emission of pollutants that affect the net wave radiation. The climate of an urban region is influenced directly by the release of the energy into the atmosphere. Therefore, the study of the energy fluxes of urban areas is essential. According to S. Rafael  there are three ways to approach and study the energy fluxes. Those are:
exploit the turbulent flux measurements produced from the Eddy Covariance (EC) method;
use a combined method with flux measurements and simulations;
use models that are designed to simulate the energy balance in the urban environment to link surface energy balance with climate.
With the third method, the surface cover can be modified to study the energy fluxes and climate behaviors among different scenarios of surface cover patterns inside a city.
The energy balance and, consequently, the energy fluxes are affected by several atmospheric variables. All net wave radiation is influenced by the solar radiation. Turbulent sensible and latent heat fluxes are influenced by atmospheric humidity, air temperature and wind speed. Soil moisture affects the latent heat, defined by precipitation. In turn, energy fluxes also affect the atmospheric variables . Modifying these variables, different scenarios of energy fluxes can be studied to improve sustainability in urban environments.
Sustainability is defined as transforming living waves for the optimal support of the environmental and social conditions to human security, well-being, and health . An urban environment's sustainability can be achieved using different alternatives such as Nature Based Solutions (NBS), or cool and highly reflective materials [12,13]. There is a strong interest in solutions that are based on natural ecosystems and lead to renaturing cities . Studies on ecosystem-based solutions seem to be useful for water management, air quality, public health, and well-being . Moving from an ecosystem-based approach to NBS to adapt and limit climate change impacts could be the solution for improving human health and well-being .
NBS determines actions that are based on the copy, the inspiration, or the support from nature . According to  NBS is the deployment of various features and processes inspired and supported by nature. This leads to the adoption of sustainability in urban, rural, and natural environments at multiple scales while dealing with social, economic, or environmental challenges providing several benefits that support sustainable development and resilience.
Similar studies have been performed with modeling tools to predict the climate in urban areas by increasing the greenery inside the urban area or using cool roofs materials and cool pavements [19,20]. Rafael et al.  use SUEWS for Porto’s city to study the behavior of energy balance inside the city according to these two scenarios. Another study in Einthoven uses NBS to assess local changes in heat fluxes and urban compaction . Also, SUEWS has been tested and evaluated in different cities like Porto [8,22], Vancouver, and Los Angeles [5,23], London and Swidon , Helsinki and Montreal , Dublin [25,26], Shanghai . The work performed in the framework of the BRIDGE project focuses on the urban metabolism using different models for the estimation of the local scale energy, water, carbon and pollutant fluxes . Similar scenarios are studied by Li et al.  to explore the urban heat island over the Baltimore-Washington metropolitan area using Princeton Urban Canopy Model. Both of them use Weather Research and Forecasting model to supply the forcing meteorological data. The results seem to be similar even though the methods differ. Besides, Manoli et al.  pinpoint that using low vegetation to reduce the temperature through evapotranspiration could work mainly in cities with dry climatic conditions but not in tropical cities with high precipitation (P >1000 mm yr-1). In tropical cities would be more useful to increase shading and ventilation than using evaporative cooling. Also, the high albedo materials, although they reduce the surface temperatures of cities, have a heating penalty during winter period [29,30].
Although significant research is performed to show NBS potential to tackle urban heat island phenomenon, most of the studies are focusing on microclimatic scale using either field studies and data or microscale modelling [31–33] or to access city scale using satellite data [34,35]. The aim of the present research is to couple field studies’ data extracted from meteorological station and EC tower with modelling techniques to evaluate the impact of NBS in city scale. The city of Heraklion, Crete, Greece is used as the case study. SUEWS model is used to investigate the energy fluxes in the center of the city [5,36–39]. The city's surface cover is changed to evaluate the energy fluxes when NBS are integrated into the city context.
The paper is structured in four sections. Section 2 includes the methodology, the study area, the data, and a description of the SUEWS model and its evaluation in the study area. The various NBS scenarios for the city of Heraklion are analyzed and discussed in Section 3. Section 4 summarizes and presents the conclusions of the overall work.
The methodology followed in the present work is depicted in Figure 1. The city of Heraklion, Crete, Greece is selected as a case study as it ensures access to:
Detailed meteorological data from a meteorological station installed in the center of the city.
Access to EC tower and energy fluxes data.
Figure 1 The methodology for the NBS evaluation in Heraklion, Crete, Greece (adapted from ).
The SUEWS model (Section 3) is utilized to simulate the various fluxes in the city of Heraklion. The developed model is evaluated versus real data extracted by the EC tower. Data set from July 2018 is used for the evaluation of the model under the specific urban conditions. To evaluate the model, meteorological and net wave radiation data are collected from the EC tower and the radiometer, respectively. The monitoring procedure and mechanisms are described in Section 2.2.
The evaluated model is then used to assess the incorporation of NBS in the urban area and improve the local environment.
Figure 3 Overview of SUEWS model processes (adapted from Ward ).
Water balance in urban areas is expressed by ,
The specialization of SUEWS is mainly for urban areas and examines seven types of surface. The first type is the paved surfaces areas, i.e., roads, pavements and car parks, the second type is buildings, the third is evergreen trees including shrubs, the fourth is deciduous trees including shrubs, the fifth is grass, the sixth is bare soil, and the seventh is open water like swimming pools, rivers, etc.
SUEWS development at first was focused on the urban water balance model  and the urban evaporation-interception scheme . The SUEWS model is further developed with the addition of the following sub-models:
The Objective Hysteresis Model (OHM) that calculates the ΔQS from .
The Net All-wave Radiation Parameterization (NARP)  that provides the Q*.
The Local-scale Urban Meteorological Parameterization Scheme (LUMPS)  that calculates an initial estimate of the atmosphere’s stability.
The benefits of SUEWS are mainly two, i.e., the simplicity of the model and the undemanding requirements for inputs compared to other models. As a result, the execution for different years and multiple grids can be accomplished using typical computing facilities . SUEWS can be characterized either as an urban-scale model for standalone use or as a tool for decision-making by customizing scenarios for the urban planners and policymakers that fit their needs .
SUEWS was initially developed, parameterized, and evaluated using data that were collected from Vancouver suburban areas [5,41,42]. Other evaluations have been performed using data from cities such as Los Angeles , Montreal and Helsinki [36,47] and Dublin .
Net all-wave radiation is modeled using all radiations,
The calculation of outgoing short-wave radiation (K↑) is performed using a bulk albedo (a). Bulk albedo is modeled using the albedo of the different types of surfaces with the area scheme's fractions. The values of the albedo used in SEUWS are selected from Oke  and modeled bulk albedo to 0.14, also used for London and Swindon  Vancouver and Los Angeles . The albedo and emissivity values are tabulated in Table 1. The specific values are not appropriate for the Heraklion site due to the lack of observational data. The methodology proposed by Ward et al.  is adopted in the present case to perform adjustments that lower the SEUWS bulk albedo due to the presence of buildings and paved surfaces. Moreover, the specific methodology enables the bulk albedo changes due to seasonal and vegetation variations (e.g., deciduous trees).
Table 1 The original values for albedo and emissivity have been used from Ward . Different albedo values have been used for leaf-off/leaf-on (Min/Max) for deciduous trees.
The calculation of incoming longwave radiation (L↓) is performed taking into account the cloud cover. Cloud cover is estimated using Tair and RH [44,49]. There is an overestimation for incoming longwave radiation that is due to the empirical relation that is used for the determination of cloud fraction :
The calculation of outgoing longwave radiation (L↑) is performed using the effective radiative surface temperature T0 and a small part from L↓,
Due to the inability of T0 determination in large areas  use an approximation replacing T0 with Tair,
Anthropogenic heat flux, QF, is the energy released as a result of human activities, including the energy released from buildings due to the use of electrical devices (for heating, cooling, etc.), transportation and the human metabolism [51,52]. QF has a significant impact on the energy demand in an urban environment [53–56]. The QF is estimated from SUEWS daily, according to Sailor and Vasireddy :
The calculation of the net change in heat storage, ΔQS, is realized with Objective Hysteresis Model (OHM) :
According to the diurnal cycle shape, the OHM seems to perform well at suburban sites during the summer months, with a small underestimation during the daytime. In the winter, there is an underestimation of ΔQS, so the coefficients have to be adjusted for two seasons . The inability of this study to evaluate ΔQs is due to the lack of observational data.
Considering the three-dimensional structure of the urban surface and including the wall area has been found that instead of improving performance using OHM, there is a decrease when the walls are essential . Even so, the expectation of building construction dependence seems to be reasonable. Arnfield and Grimmond  created a numerical model to nominate α1,3 coefficients' dependence with the increase of height-to-width ratio and materials density in buildings. The importance of considering the characteristics of buildings for the selection of appropriate coefficients values to set OHM is evident. Several values have been tested to connect with the environment and the urban structure [47,59,66].
Ward  presented a new functional dependence on the control functions  with the main objective of relaxing the control of soil moisture on evaporation. With the new set of parameters, the limiting Δθ is much larger and thus more suitable for the Heraklion site. The new relations are designed to be less restrictive so that unrealistic values of the surface conductance (and QE) are avoided. In suburban areas, the diurnal pattern of both observed and modeled g functions exhibit the expected behavior during summer, mainly determined by the changing ratio of VPD/QE where VPD is the vapor pressure deficit. Conductances are much smaller and have a less clearly defined pattern in areas with less vegetation, reduced moisture availability, and lower evaporation rates. In winter, the diurnal cycle is shorter, more symmetrical, and smaller in amplitude at suburban areas, while observed night-time values are higher at urban and suburban sites, probably due to higher wind speeds and damp surfaces. Observed g functions are also higher than suggested by the model during winter daytimes .
The turbulent sensible Heat flux is calculated as the residual of energy balance:
The study focuses on the city of Heraklion (35°20'N, 25°8'E). The city of Heraklion is the largest city on Crete and one of the larger in Greece. Heraklion has been selected in the past as a test urban area for the measurement of turbulent fluxes using a specific EC tower installed in the framework of URBANFLUXES project , which is part of the International Association for Urban Climate Urban Flux Network . Heraklion is selected due to its cover/morphology, climate, and traffic/commuter. According to Koppen climate classification  Heraklion has a hot summer Mediterranean type Csa. The average temperatures in summer peaks can reach over 40 C° with a mean daily average of 25.7 C° for the July, the hottest month. In the summer days are warm to hot and dry (relative humidity drops from 68% in winter to 56.3% in the summer) with clear skies often with seasonal breezes. The types and properties of buildings are highly variable across the city. Residential neighborhoods and commercial areas are included inside the city center mix of low and mid-rise buildings. Most of the buildings in the city center’s residential area are mainly old without any thermal insulation or building services such as heating ventilation and air conditioning. In the center of the city, buildings’ density is quite high (40-70%) while it is lower in the suburbs (10-30%). The EC tower has been installed in the core of the Heraklion city center (35°20'10''N, 25°8'58''E, elevation 30m above sea level . The position of the EC tower is marked with a yellow cross in Figure 4.
The required data for the present study are:
Meteorological and radiation data from the sensors installed in the EC tower (hourly averaged data). The meteorological station is a Campbell Scientific IRGASON and measures the following:
short-wave radiation (K↓)
air temperature (Tair)
relative humidity (RH)
wind speed (U)
Population density data from the national statistical services.
Surface cover data. In this study Surface Cover data and Digital Surface Model (DSM) for buildings and trees are extracted from remote sensing [73,77]. Roughness length and displacement height are extracted from DSMs for trees and buildings using the Morphometric Calculator tool from UMEP processing tools. Morphometric Calculator tool is a plugin of UMEP environment that uses DSM to calculate morphometric parameters that are used the descriptions of the roughness of the surface and are included in various local and mesoscale climate models [46,78]. Due to the lack of initial soil moisture data, the value is set equal to 100% .
Α Kipp & Zonnen CNR1 Net Radiometer is installed to measure the fluxes used in the specific work.
The characteristics of the site are tabulated in Table 2.
The model evaluation focuses on the local/neighborhood scale fluxes at 100m radius around the EC tower’s location. In Figure 4 the green dotted line defines the evaluation area. The EC observation source area depends on wind speed, direction, and stability . The EC fluxes' origination area is calculated from the footprint model a few hundred meters of the flux tower. The upwelling source area of EC outlines are depicted in white color (60%,70%,80%,90%), and the radiometer isopleth is marked with the orange circle (Figure 4).
The SUEWS model used for the simulation is 3.15.4 UMEP and is running offline with 5 min time-step. The resolution of the various meteorological data that are required for the model is 60 min. Linear interpolation is performed in a 5 min interval for all data apart from precipitation that is considered constant for a 60 min. The model outputs are averaged to 60 min to be comparable with the obtained observational data . The indicated evaluation area is selected to match the source area of the radiometer for Q*  and urban fluxes (Figure 4). The model evaluation is performed using the data collected from the CNR1 radiometer and the EC tower collected from May to October 2018.
The variables used for the model evaluation are all radiation variables. Root Mean Square Error (RMSE) and determination (R2) are used to compare the modeled versus the measured variables.
The evaluation results for each variable are discussed in the following sub-sections.
The measured vs modeled K↓, K↑, L↓, L↑ is depicted in Figure 5. The net-all wave radiation evaluation results are depicted in Figure 6. The Q* model approximation is very high, with R2 reaching 0.99 and a scatter that is really small (RMSE = 54 W m-2). The errors of L↓ and K↑ are those that mostly affect the differences of modeled versus measured Q*. The underestimation of K↑ is due to the area structure of radiometers footprint so the radiometer also has an error from the real reflected short-wave radiation. The error of L↓ is attributed to the estimation of the cloud cover; using the L↓ observational data Q* may be improved. It is important that simulation results have the same behavior as observational data. The study aims to investigate the behavior of different scenarios in the surrounding area of urban and suburban environments (see Figure 1).
The Q*overestimation (Figure 7) is attributed to the following factors:
The existence of street canyons in the larger source area of the CNR1 radiometer affects the overall bulk albedo (≈0.2) . The model used for the calculation of the albedo does not include the morphology of the area. The lack of optical interaction between paved and CNR1 radiometer overestimates the observational albedo .
The CNR1 radiometer is installed in a site to provide radiative fluxes that represent the EC footprint. On the other hand, the EC footprint is dynamic. It depends on the site’s characteristics and meteorological conditions (wind, etc.) instead of the radiometer’s source area that is much smaller and constant in time and space.
The monitored versus modeled turbulent sensible heat flux is depicted in Figure 8. Turbulent sensible heat flux is calculated as the residual of energy balance. Therefore, the error of each one heat flux is accumulated to turbulent sensible heat flux. The RMSE is 61.36 W m-1, which is reasonable due to error propagation and the EC’s irregular source area that changes by time according to the wind speed, wind direction, atmospheric stability, and lateral dispersion qualities of the flow .
A similar study in Porto with a warm-summer Mediterranean climate type Csb in Koppen showed differences between urban and suburban areas outside the city . Compared to Heraklion, Porto has a lower average temperature during hot months with more wet winters with an average of 1.237 mm/year precipitation instead of 500.6 mm/year for Heraklion. The forcing data were modeled using the Weather Research and Forecasting Model (WRF) and compared with EC tower data. The final RSME of QH for urban areas reaches 85.9 W/m2 and 49.4 W/m2 for the suburban area for 1 August – 31 December 2014. The results showed a slightly better performance for the model when it is applied in suburban areas. Moreover, the model seemed to perform better in the specific study for measured forcing data.
Another study in Helsinki , with humid continental climate type Dfb according to Koppen, took place in two sites, an urban and a suburban area. The results errors for the summertime were close to Porto and Heraklion. The RMSE for QH during summer was 67.1 W/m2 and 52.9 W/m2 for the urban and suburban areas, respectively. The forcing data were extracted from EC towers, and the modeled data were compared with fluxes from EC tower. The error for the period of autumn in Helsinki was 38.3 W/m2 and 27.1 W/m2 for the urban and suburban areas, respectively. The study at Helsinki showed that the model seems to perform better for the autumn-winter compared to spring-summer.
Even using the basic parameterization and forcing data, the model seems to perform well for different areas. The low requirements of input data make the model suitable for several studies that focus on surface energy balance.
Following the SUEWS model development and evaluation for Heraklion, the evaluated model is utilized to analyze the impact of NBS on the specific urban area. The NBS that are proposed are mainly linked with greenery and water bodies development. Five different NBS scenarios are simulated and studied:
Replacement of pavements with trees
Replacement of roofs with grass
Replacement of pavements with grass
A mixture of replacement of roofs with grass and pavements with trees
Replacement of pavements with water bodies
The scenarios mentioned above change the evapotranspiration and the city area’s albedo, leading to changes in the energy fluxes. Increasing the city’s green areas, the turbulent latent heat flux increases and turbulent sensible heat flux decreases, leading to evaporative cooling. The changes in energy fluxes directly impact the air temperature and relative humidity (see Figure 3). The changes are discussed in the following subsections.
The surface cover fractions are changed in the specific scenario versus the baseline conditions tabulated in Table 2. Three different conditions are examined where different fractions are analyzed, i.e., 10%, 20% and 30% increase of trees. The type of trees used are deciduous trees. The changes of air temperature in 2m height in the simulation area when replacing pavements with trees is depicted in Figure 9. The specific figure illustrates the air temperature for two days during the summer period when the urban overheating is present. The diurnal cycles for QH and QE for two days per each month during the summer period are illustrated in Figure 10 and Figure 11 respectively. The two days cycle is selected to show the diurnal cycle of air temperature, sensible and latent heat fluxes changes. The overall changes on a monthly basis for air temperature, relative humidity and fluxes are tabulated in Table 3.
Figure 9 Air temperature for 2 days during May, June, July and August for the replacement of pavements with trees.
Figure 10 Sensible heat flux for 2 days during May, June, July and August for the replacement of pavements with trees.
Figure 11 Turbulent Latent heat flux for 2 days during May, June, July and August for the replacement of pavements with trees.
Table 3 Percentage of changes for all fluxes and for air temperature and relative humidity during the daytime on a monthly basis.
By replacing the existing pavements with deciduous trees, a reduction of air temperature occurs during the daytime. The decrease of air temperature peaks although it is almost 0.5K occurs for all months and for the 20% replacement of pavements as expected. A steep decrease of the air temperature occurs during nighttime for all months which is almost 1-2K. The air temperature when trees are inserted peaks faster than the normal (baseline scenario) before midday instead of the baseline scenario that temperature exceeds the trees scenario during the afternoon. As expected, the increase of greenery reduces the sensible heat flux peaks during daytime and increases the turbulent latent heat fluxes respectively.
The diurnal cycles of the sensible and latent heat fluxes with the increase of greenery follow closely the baseline patterns for all days in the summer period. The reduction of the sensible heat flux is higher during the daytime for both days and for all summer months and can be among 100-200 W/m2 in peak hours. During night the decrease of sensible heat flux is not so significant. By increasing the greenery from 10% to 20% the decrease of the sensible heat flux during nighttime is negligible.
A decrease of Q* and ΔQs is noticed for all months (Table 3). Moreover, there is a decrease of turbulent sensible heat flux from the city to the atmosphere (QH) about 150 to 200 W/m2 depending the period (Figure 10) due to the other fluxes' changes as explained in Eq. 12. An increase of the turbulent latent heat flux (Figure 11), which is almost equal to the decrease of sensible heat flux, indicates an increase of evapotranspiration around the trees and an increase of the relative humidity. When the decrease of QH is high, the Q* and ΔQs decrease is low and vice versa. For example, during July the percentage decrease of QH is the lowest one for both 10% and 20% replacement of pavements with greenery. During that month the Q* and ΔQs have their highest percentage of changes. This is in accordance with Eq. 12. The reduction of the Q* is attributed to the increase of the surface covers’ albedo when trees replace the pavements. The reduction of Q* follows the percentage of reduction of the heat storage in the areα for all months. The increase of greenery has a direct impact on the increase of latent turbulent heat flux as expected. The percentage of increase of QE is almost the same for all months and is on average 106% for 10% increase of greenery and 165% for 20% increase of greenery. Therefore, the presence of greenery significantly influences the fluxes’ energy balance which explains the increase of relative humidity and the slight decrease of the air temperature.
In this scenario, the roofs of the city area of Heraklion are replaced with green roofs. Although green roofs have a different behavior than grass, the species used for green roofs in the area of Greece are local species similar to ones cultivated using ground soil. Moreover, in the scale that the scenario is analyzed the presence of greenery in rooftops changes the balance among the various fluxes as we can see from the results.
To simulate this scenario, the concrete roofs are replaced with grass. Three different fractions are examined in this scenario, i.e., 10%, 20% and 30% replacement of roofs’ area with grass.
The changes in air temperature at 2m height for two days in the summer period are depicted in Figure 12, while the changes in sensible and turbulent latent heat flux are illustrated in Figure 13 and Figure 14 respectively. The air temperature peaks are decreasing while the percentage of green roofs is increasing. During peak daytime hours, the air temperature decrease can reach up to 1-1.5K. The diurnal fluctuations of the air temperature do not change with the presence of the green roofs. Also during the early night hours (e.g. between 22:00 to 4:00) the temperature is not significantly decreased with 10 or 20% increase of greenery while there is a significant decrease with 30% replacement of roofs with greenery. On the other hand, we can notice a considerable decrease of air temperature between 4:00–8:00.
Figure 12 Air temperature for 2 days during May, June, July and August for the replacement of roofs with green roofs.
Figure 13 Sensible heat flux for 2 days during May, June, July and August for the replacement of roofs with green roofs.
Figure 14 Turbulent latent heat flux for 2 days during May, June, July and August for the replacement of roofs with green roofs.
The sensible heat flux is decreased as depicted in Figure 13 especially during daytime. During night the levels of sensible heat flux are not changed in a great extent. Another critical aspect is the fact that with the presence of greenery the lower values of sensible heat flux occur during late afternoon which is very important for the urban context.
Again, the increase of the turbulent latent heat flux (Figure 14) is present in this scenario, from 150 W/m2 for May to 350 W/m2 for July for the scenario of changing 10%. This difference increases along with the rise of green roofs area, while a significant increase of relative humidity is noticed that can reach 33.32%.
The replacement of roofs with grass reduces the Q* since the surface albedo increases (Table 4). Moreover, the changes of air temperature at 2m height are illustrated, showing a significant decrease for this scenario that during daytime can reach up to 1-1.5K. This is also tabulated in Table 4 where for daytime during May a decrease of air temperature of 0,8% is noticed while in July and August this percentage reaches 2.2% and 1.8% respectively. The highest decrease is pinpointed when a 30% increase in grass is performed. The decrease of sensible heat flux due to the replacement of the roofs with green roofs is in accordance with similar previous publications that studied the performance of green roofs and their urban heat mitigation potential .
In this specific scenario, three alternatives are examined, i.e., 10%, 20% and 30% replacement of pavements with grass. The impact of the replacement of pavement with grass on the air temperature is depicted in Figure 15. The decrease of air temperature reaches almost 1K for 30% replacement of grass for July. The fluxes differences on a monthly basis are tabulated in Table 5. The reduction of QH (Figure 16) is attributed to the overall balance of the fluxes based on Eq. 12, and it is mainly due to the significant increase of the turbulent latent heat flux and the decrease of the net wave radiation for the box of Figure 2. Again, the results are similar to the previous case of replacing roofs with grass. The turbulent latent heat flux increases between 200 and 400 W/m2 proportionally to the replacement of grass (Figure 17), and the sensible heat is decreased between 100 and 250 W/m2 (Figure 16). In almost all cases a slight increase of relative humidity is noticed.
Figure 15 Air temperature for 2 days during May, June, July and August for the replacement of pavements with grass.
Figure 16 Sensible heat flux for 2 days during May, June, July and August for the replacement of pavements with grass.
Table 5 Percentage of changes for all fluxes and for air temperature and relative humidity during the daytime on a monthly basis for the replacement of pavements with grass.
The specific scenario is a combination of two of the above and allows the study of the interrelations between the various NBS alternatives. The temperature decrease also occurs in this scenario and exceeds 1K during daytime, as depicted in Figure 18. The percentage of change for the air temperature monthly and during the daytime is greater than 2% except for May, which shows a smaller decrease. The decrease of Q* is slightly higher than the one noticed when the roofs are replaced with grass due to the reduction of surface albedo for replacing higher percentage area of buildings and paved using higher albedo fractions (Table 6). The turbulent latent heat flux has the same behavior with the 1st scenario where the pavements are replaced with trees and grass, and it is in accordance with the replacement of roofs with grass. The decrease of the QH in Figure 19 is explained by the increase of the turbulent latent heat flux in Figure 20. The results are similar with scenarios 2 and 3, when grass replaces 30% of roof or pavements. This is attributed to the fact that trees are combined with grass replacing paved and roofs. A very small air temperature decrease occurs when trees and grass replace pavements and roofs respectively, as depicted in Figure 18. The temperature difference exceeds 1K.
Figure 18 Air temperature for 2 days during May, June, July and August for the replacement of pavements with trees and grass and roofs with grass.
Table 6 Percentage of changes for all fluxes and for air temperature and relative humidity during the daytime on a monthly basis for the replacement of pavements with trees and roofs with grass.
Figure 19 Sensible heat fluxes for 2 days during May, June, July and August for the replacement of pavements with trees and grass and roofs with grass.
Although replacing pavements with water bodies is very difficult to be applied in the specific site, the changes that occur are examined. The air temperature during two days is depicted in Figure 21, the sensible heat flux in Figure 22 and the turbulent latent heat flux in Figure 23. The fluxes differences on a monthly basis are tabulated in Table 7. A negligible air temperature decrease is noticed when water bodies are used during daytime. Also during July, the differences are higher, as depicted in Table 7. The Q* is increased due to the decrease in water albedo versus the pavements. The same occurs for QE as expected, and during daytime evaporation is increased, about 40 W/m2 for May, the RH is increased by 1.09% due to QΕ increase. A decrease occurs for QH over 100 W/m2, almost the same amount of QΕ rise, for all months based on the Eq. 12.
Figure 21 Air temperature for 2 days during May, June, July and August for the replacement of pavements with water.
Figure 22 Sensible heat flux for 2 days during May, June, July and August for the replacement of pavements with water.
Figure 23 Turbulent Latent heat flux for 2 days during May, June, July and August for the replacement of pavements with water.
Table 7 Percentage of changes for all fluxes and for air temperature and relative humidity during the daytime on a monthly basis for the replacement of pavements with water.
The NBS scenarios simulation results using SUEWS model shows that albedo has a considerable impact on the net-all wave radiation as expected and seems to be a critical parameter when urban overheating mitigation measures are adopted. The application of NBS in the specific site shows that for all cases, i.e., green infrastructure or water bodies, the air temperature at 2m height is reduced versus the baseline. The percentage of replacement also plays a critical role in the improvement of climatic conditions at the site. Moreover, the lower the K↑ the higher the Q* and ΔQS are the energy that is trapped inside the buildings.
Since the version of the model we use does not allow to change the pavements' albedo, the interrelation of both highly reflective materials with NBS is not possible. Nevertheless, it should be noted that although with the replacement of pavements with trees, the albedo reduces, the air temperature at 2m high is lower than the baseline. Therefore, incorporating highly reflective materials and green infrastructure will have a significant impact on reducing air temperature and surface temperature. An interesting finding is the impact of grass on the air temperature and relative humidity. The selection of the most appropriate solutions should consider the relative humidity of the air in the site to not create discomfort due to the increase of relative humidity. The grass seems to positively impact lowering air temperature without changing the relative humidity of the air significantly. Concerning water bodies, it is well recognized that they contribute to the decrease in air temperature, but relative humidity changes are considerable.
On the other hand, precipitation, irrigation, and water availability should be a significant concern in applying water bodies in cities. Using grass to replace pavements shows a reduction in the air temperature that reaches 2.27%, but also using grass can achieve a decrease of 1.82%. This reduction could be due to changes in the albedo that help reduce the energy balance. The rise of the turbulent latent heat flux that reaches almost 200% and the sensible heat flux reduction that reaches 100% shows that the amount of sensible heat fluxes is replaced by turbulent latent heat fluxes. This is also shown in the figures of sensible and turbulent latent heat flux (Figures 13,14,16,17,19,20). The turbulent latent heat flux from 120 W/m2 in the baseline reaches 500 W/m2 in some scenarios, and the sensible heat flux reduces to 100 W/m2. The highest rise in turbulent latent heat flux is during August for combined roof and pavements to grass and trees that exceed 200%, and this is probably due to the significant change in the surface cover, i.e., the percentage of buildings and pavements is reduced from 95% to 55%. Using trees, there is a reduction about 0.79% to the temperature but not as high as when grass is used. This is probably due to the lower rise on albedo when the trees are applied, so the net radiation is higher than applying grass. The evapotranspiration increases more using grass. As a result, the sensible heat flux is also (Figure 10) higher (100W/m2) during the day instead of applying grass that, in every case, is lower than 100W/m2 (Figures 13,16,19).
The urban climate model SUEWS is evaluated and used for NBS cases to modulate the microclimate inside the local urban environment. The site of interest is a dense metropolitan area in Heraklion, Greece, and consists of buildings and pavements mostly (≈93%). The model simulates the energy balance using meteorological data and incoming short-wave radiation.
The overall approach and modeling procedure has been performed according to the literature to allow comparisons with other studies. The model has a user-friendly interface, while primary remote sensing and meteorological data can provide valuable results concerning the urban fluxes. Modifying the remote sensing data, as landcover and DEMs, can offer any scenario for different site structures for several study purposes.
The use of the simple SUEWS model version used in the present study has a limitation concerning the parameterization for the characteristics of the surface components. The advanced version requires more data related to the characteristics of elements for each sub-model, anthropogenic parameters and water use of the modeled site that increase the parametrization complexity.
The study shows that incorporating green infrastructure in various forms in a densely built city reduces the air temperature above ground while decreasing the sensible heat flux considerably. The air temperature and sensible heat flux are decreased according to the increase of the various spaces’ green coverage. Α significant finding is the change of relative humidity and turbulent latent heat flux, which seems very high in some cases especially when the grass is incorporated. This may significantly impact the urban comfort levels and be correlated with the local climatic conditions.
Although challenging to incorporate in urban outdoor spaces, water bodies have a considerable impact on the urban environment regarding outdoor temperature reduction and relative humidity. At the same time, they create pleasant spaces for citizens.
Therefore, the incorporation of NBS in cities change the energy balances significantly and modify the urban environment for the citizens’ benefit. The quantification of the NBS impact on citizens’ comfort and health is of great importance. At the same time, it contributes to reducing the energy demand for cooling and mitigation of the urban heat island.
The research is not funded by any institution
The authors have declared that no competing interests exist
EP: simulations, data analysis, results presentations.DK: data analysis, supervision, discussion and conclusions. NC: data quality, discussion, conclusions.
The authors would like to thank Dr Stavros Stagakis for his useful comments regarding the data processing of the EC tower and validation procedures.
|Q*||Net all-wave radiation||W/m2|
|QF||Anthropogenic heat flux||W/m2|
|QH||Turbulent Sensible heat flux||W/m2|
|QE||Turbulent Latent heat flux||W/m2|
|ΔQs||Net storage heat flux||W/m2|
|Ie||Water supplied by irrigation or street cleaning|
|ΔS||Net change in the water storage|
|K↓||Short-wave incoming radiation||W/m2|
|K↑||Short-wave outgoing radiation||W/m2|
|L↓||Longwave incoming radiation||W/m2|
|L↑||Longwave outgoing radiation||W/m2|
|ε0||Emissivity of surface|
|σ||Constant of Stefan-Boltzmann|
|αF0,1,2||Anthropogenic heat flux coefficients|
|f||Surface cover fraction|
|ρ||Density of air||kg m−3|
|cρ||Specific heat capacity of air at constant pressure||J kg−1 K−1|
|VPD||Vapor pressure deficit||Pa|
|rav||aerodynamic resistance||m s−1|
|rs||Surface resistance||m s−1|
|rav||Rate at which water vapor is transported by turbulence between the surface and atmosphere||m s−1|
|S||slope of the saturation vapour pressure curve||Pa K−1|
|γ||Psychrometric constant||Pa K−1|
|LAI||Leaf area index|
|g||Control exerted function|
|G1||Constant for gs|
|Δθ||Soil moisture deficit|
|Δq||Specific humidity deficit|
|1.||UN Department of Economics and Social Affairs. World Population Prospects - Population Division - United Nations. Int J Logist Manag 2015. [Google Scholar]|
|2.||Caprotti F, Cowley R, Datta A, Broto VC, Gao E, Georgeson L, et al. The New Urban Agenda: key opportunities and challenges for policy and practice. Urban Res Pract 2017;10:367-378. [Google Scholar] [CrossRef]|
|3.||Ward HC, Kotthaus S, Järvi L, Grimmond CSB. Surface Urban Energy and Water Balance Scheme (SUEWS): Development and evaluation at two UK sites. Urban Clim 2016;18:1-32. [Google Scholar] [CrossRef]|
|4.||Wilby RL. Constructing climate change scenarios of urban heat island intensity and air quality. Environ Plann B Plann Des 2008;35(5):902-919. [Google Scholar] [CrossRef]|
|5.||Järvi L, Grimmond CSB, Christen A. The Surface Urban Energy and Water Balance Scheme (SUEWS): Evaluation in Los Angeles and Vancouver. J Hydrol 2011;411(3–4):219-237. [Google Scholar] [CrossRef]|
|6.||Chrysoulakis N, Castro E, Moors E, editor. Understanding Urban Metabolism: A Tool for Urban Planning. New York, USA: Routledge; 2015. [Google Scholar] [CrossRef]|
|7.||Rafael S, Martins H, Sá E, Carvalho D, Borrego C, Lopes M. Influence of urban resilience measures in the magnitude and behaviour of energy fluxes in the city of Porto (Portugal) under a climate change scenario. Sci Total Environ 2016;566–567:1500-1510. [Google Scholar] [CrossRef]|
|8.||Rafael S, Martins H, Marta-Almeida M, Sá E, Coelho S, Rocha A, et al. Quantification and mapping of urban fluxes under climate change: Application of WRF-SUEWS model to Greater Porto area (Portugal). Environ Res 2017;155(October 2016):321-334. [Google Scholar] [CrossRef]|
|9.||Vargo J, Habeeb D, Stone B. The importance of land cover change across urban-rural typologies for climate modeling. J Environ Manage 2013;114:243-252. [Google Scholar] [CrossRef]|
|10.||Bonan GB. Ecological Climatology: Concepts and Applications. Cambridge, UK: Cambridge University Press; 2002. [Google Scholar]|
|11.||Martine G, Marshall A. State of world population 2007: Unleashing the potential of urban growth. In Stateof World Population: Unleashing the Potential of Urban Growth. New York, USA: UNFPA; 2007. [Google Scholar]|
|12.||Li D, Bou-Zeid E, Oppenheimer M. The effectiveness of cool and green roofs as urban heat island mitigation strategies. Environ Res Lett 2014;9(5):055002. [Google Scholar] [CrossRef]|
|13.||Kyriakodis GE, Santamouris M. Using reflective pavements to mitigate urban heat island in warm climates - Results from a large scale urban mitigation project. Urban Clim 2017;24:326-339. [Google Scholar] [CrossRef]|
|14.||Huynh Q, Craig W, Janssen I, Pickett W. Exposure to public natural space as a protective factor for emotional well-being among young people in Canada. BMC Public Health 2013;13:407. [Google Scholar] [CrossRef]|
|15.||Kolokotsa D, Lilli AΑ, Lilli MA, Nikolaidis NP. On the impact of nature-based solutions on citizens’ health & well being. Energy Build 2020;229:110527. [Google Scholar] [CrossRef]|
|16.||Raymond CM, Frantzeskaki N, Kabisch N, Berry P, Breil M, Nita MR, et al. A framework for assessing and implementing the co-benefits of nature-based solutions in urban areas. Environ Sci Policy 2017;77(July):15-24. [Google Scholar] [CrossRef]|
|17.||European Commission. Report from the Commission to the European Parliament and the Council - on the implementation of the EU Strategy on adaptation to climate change. 2018. COM(2018) 738 final.|
|18.||Somarakis G, Stagakis S, Chrysoulakis N. Thinknature Nature-Based Solutions Handbook. ThinkNature project funded by the EU Horizon 2020 research and innovation programme 2019;:730338. [Google Scholar]|
|19.||Akbari H, Cartalis C, Kolokotsa D, Muscio A, Pisello AL, Rossi F, et al. Local climate change and urban heat island mitigation techniques - The state of the art. J Civ Eng Manag 2016;22(1):1-16. [Google Scholar] [CrossRef]|
|20.||Kolokotsa DD, Giannariakis G, Gobakis K, Giannarakis G, Synnefa A, Santamouris M. Cool roofs and cool pavements application in Acharnes, Greece. Sustain Cities Soc 2018;37:466-474. [Google Scholar] [CrossRef]|
|21.||Augusto B, Roebeling P, Rafael S, Ferreira J, Ascenso A, Bodilis C. Short and medium- to long-term impacts of nature-based solutions on urban heat. Sustain Cities Soc 2020;57:102122. [Google Scholar] [CrossRef]|
|22.||Rafael S, Rodrigues V, Fernandes AP, Augusto B, Borrego C, Lopes M. Evaluation of urban surface parameterizations in WRF model using energy fluxes measurements in Portugal. Urban Clim 2019;28:100465. [Google Scholar] [CrossRef]|
|23.||Kokkonen TV, Grimmond CSB, Räty O, Ward HC, Christen A, Oke TR, et al. Sensitivity of Surface Urban Energy and Water Balance Scheme (SUEWS) to downscaling of reanalysis forcing data. Urban Clim 2018;23:36-52. [Google Scholar] [CrossRef]|
|24.||Järvi L, Grimmond CSB, Taka M, Nordbo A, Setälä H, Strachan IB. Development of the Surface Urban Energy and Water Balance Scheme (SUEWS) for cold climate cities. Geosci Model Dev 2014;7(4):1691-1711. [Google Scholar] [CrossRef]|
|25.||Alexander PJ, Fealy R, Mills GM. Simulating the impact of urban development pathways on the local climate: A scenario-based analysis in the greater Dublin region, Ireland. Landsc Urban Plan 2016;152:72-89. [Google Scholar] [CrossRef]|
|26.||Alexander PJ, Fealy R, Mills G. Spatial validation of an urban energy balance model using multi-temporal remotely sensed surface temperature. 2015 Joint Urban Remote Sensing Event (JURSE) 2015. [Google Scholar] [CrossRef]|
|27.||Ao X, Grimmond CSB, Ward HC, Gabey AM, Tan J, Yang XQ, et al. Evaluation of the Surface Urban Energy and Water Balance Scheme (SUEWS) at a dense urban site in Shanghai: Sensitivity to anthropogenic heat and irrigation. J Hydrometeorol 2018;19(12):1983-2005. [Google Scholar] [CrossRef]|
|28.||Manoli G, Fatichi S, Schläpfer M, Yu K, Crowther TW, Meili N, et al. Magnitude of urban heat islands largely explained by climate and population. Nature 2019;573(7772):55-60. [Google Scholar] [CrossRef]|
|29.||Gobakis K, Kolokotsa D, Maravelaki-Kalaitzaki N, Perdikatsis V, Santamouris M. Development and analysis of advanced inorganic coatings for buildings and urban structures. Energy Build 2015;89:196-205. [Google Scholar] [CrossRef]|
|30.||Kolokotsa D, Diakaki C, Papantoniou S, Vlissidis A. Numerical and experimental analysis of cool roofs application on a laboratory building in Iraklion, Crete, Greece. Energy Build 2012;55:85-93. [Google Scholar] [CrossRef]|
|31.||Skoulika F, Santamouris M, Kolokotsa D, Boemi N. On the thermal characteristics and the mitigation potential of a medium size urban park in Athens, Greece. Landsc Urban Plan 2014;123:73-86. [Google Scholar] [CrossRef]|
|32.||Makido Y, Hellman D, Shandas V. Nature-based designs to mitigate urban heat: The efficacy of green infrastructure treatments in Portland, Oregon. Atmosphere 2019;10(5):282. [Google Scholar] [CrossRef]|
|33.||Vaz Monteiro M, Doick KJ, Handley P, Peace A. The impact of greenspace size on the extent of local nocturnal air temperature cooling in London. Urban For Urban Green 2016;16:160-169. [Google Scholar] [CrossRef]|
|34.||Lin Y, Wang Z, Jim CY, Li J, Deng J, Liu J. Water as an urban heat sink: Blue infrastructure alleviates urban heat island effect in mega-city agglomeration. J Clean Prod 2020;262:121411. [Google Scholar] [CrossRef]|
|35.||Xie Q, Li J. Detecting the cool island effect of urban parks in Wuhan: A city on rivers. Int J Environ Res Public Health 2021;18(1):132. [Google Scholar] [CrossRef]|
|36.||Järvi L, Grimmond CSB, Taka M, Nordbo A, Setälä H, Strachan IB. Development of the Surface Urban Energy and Water Balance Scheme (SUEWS) for cold climate cities. Geosci Model Dev 2014;7(4):1691-1711. [Google Scholar] [CrossRef]|
|37.||Borrego C, Monteiro A, Martins H, Ferreira J, Fernandes AP, Rafael S, et al. Air quality plan for ozone: an urgent need for North Portugal. Air Qual Atmos Health 2016;9(5):447-460. [Google Scholar] [CrossRef]|
|38.||Monteiro A, Ferreira J, Ribeiro I, Fernandes AP, Martins H, Gama C, Miranda AI. Air quality over portugal in 2020. Atmos Pollut Res 2015;6(5):788-796. [Google Scholar] [CrossRef]|
|39.||Rafael S, Martins H, Sá E, Carvalho D, Borrego C, Lopes M. Influence of urban resilience measures in the magnitude and behaviour of energy fluxes in the city of Porto (Portugal) under a climate change scenario. Sci Total Environ 2016;566-567:1500-1510. [Google Scholar] [CrossRef]|
|40.||Oke TR. Boundary layer climates. 2nd edition. Oxfordshire, UK: Routledge; 1987. [Google Scholar]|
|41.||Grimmond CSB, Oke TR, Steyn DG. Urban Water Balance: 1. A Model for Daily Totals. Water Resour Res 1986;22(10):1397-1403. [Google Scholar] [CrossRef]|
|42.||Grimmond CSB, Oke TR. An evapotranspiration‐interception model for urban areas. Water Resour Res 1991;27(7):1739-1755. [Google Scholar] [CrossRef]|
|43.||Grimmond CSB, Cleugh HA, Oke TR. An objective urban heat storage model and its comparison with other schemes. Atmos Environ B Urban Atmos 1991;25(3):311-326. [Google Scholar] [CrossRef]|
|44.||Offerle B, Grimmond CSB, Oke TR. Parameterization of net all-wave radiation for urban areas. J Appl Meteorol 2003;42(8):1157-1173. [Google Scholar] [CrossRef]|
|45.||Grimmond CSB, Oke TR. Turbulent heat fluxes in urban areas: Observations and a local-scale urban meteorological parameterization scheme (LUMPS). J Appl Meteorol 2002;41(7):792-810. [Google Scholar] [CrossRef]|
|46.||Lindberg F, Grimmond S, Onomura S, Järvi LJ. Urban Multi-scale Environmental Predictor– An integrated tool for urban climatology and climate sensitive planning applications. ICUC9 - 9th International Conference on Urban Climate jointly with 12th Symposium on the Urban Environment; July 20-24, 2015; Toulouse, France..|
|47.||Karsisto P, Fortelius C, Demuzere M, Grimmond CSB, Oleson KW, Kouznetsov R, et al. Seasonal surface urban energy balance and wintertime stability simulated using three land-surface models in the high-latitude city Helsinki. Q J R Meteorol 2016;142(694):401-417. [Google Scholar] [CrossRef]|
|48.||Alexander PJ, Mills G, Fealy R. Using LCZ data to run an urban energy balance model. Urban Clim 2015;13:14-37. [Google Scholar] [CrossRef]|
|49.||Loridan T, Grimmond CSB. Characterization of energy flux partitioning in urban environments: Links with surface seasonal properties. J Appl Meteorol Climatol 2012;51(2):219-241. [Google Scholar] [CrossRef]|
|50.||Crawford TM, Duchon CE. An improved parameterization for estimating effective atmospheric emissivity for use in calculating daytime downwelling longwave radiation. J Appl Meteorol 1999;38(4):474-480. [Google Scholar] [CrossRef]|
|51.||Chrysoulakis N, Grimmond CSB. Understanding and reducing the anthropogenic heat emission. In Urban Climate Mitigation Techniques. 27. Oxfordshire, UK: Routledge; 2016. 40 p. [Google Scholar] [CrossRef]|
|52.||Sailor DJ, Georgescu M, Milne JM, Hart MA. Development of a national anthropogenic heating database with an extrapolation for international cities. Atmos Environ 2015;118:7-18. [Google Scholar] [CrossRef]|
|53.||Kłysik K. Spatial and seasonal distribution of anthropogenic heat emissions in Lodz, Poland. Atmos Environ 1996;30(20):3397-3404. [Google Scholar] [CrossRef]|
|54.||Taha H. Urban climates and heat islands: Albedo, evapotranspiration, and anthropogenic heat. Energy Build 1997;25(2):99-103. [Google Scholar] [CrossRef]|
|55.||Hamilton IG, Davies M, Steadman P, Stone A, Ridley I, Evans S. The significance of the anthropogenic heat emissions of London’s buildings: A comparison against captured shortwave solar radiation. Build Environ 2009;44(4):807-817. [Google Scholar] [CrossRef]|
|56.||Ichinose T, Shimodozono K, Hanaki K. Impact of anthropogenic heat on urban climate in Tokyo. Atmos Environ 1999;33(24–25):3897-3909. [Google Scholar] [CrossRef]|
|57.||Sailor DJ, Vasireddy C. Correcting aggregate energy consumption data to account for variability in local weather. Environ Model Softw 2006;21(5):733-738. [Google Scholar] [CrossRef]|
|58.||Ward HC, Evans JG, Grimmond CSB. Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK. Atmospheric Chem Phys 2013;13(9):4645-4666. [Google Scholar] [CrossRef]|
|59.||Doll D, Ching JKS, Kaneshiro J. Parameterization of subsurface heating for soil and concrete using net radiation data. Bound-Layer Meteorol 1985;32(4):351-372. [Google Scholar] [CrossRef]|
|60.||McCaughey JH. Energy balance storage terms in a mature mixed forest at Petawawa, Ontario - A case study. Bound-Layer Meteorol 1985;31(1):89-101. [Google Scholar] [CrossRef]|
|61.||Yoshida A, Tominaga K, Watatani S. Field measurements on energy balance of an urban canyon in the summer season. Energy Build 1990;15(3–4):417-423. [Google Scholar] [CrossRef]|
|62.||Arnfield AJ, Grimmond CSB. An urban canyon energy budget model and its application to urban storage heat flux modeling. Energy Build 1998;27(1):61-68. [Google Scholar] [CrossRef]|
|63.||Meyn SK, Oke TR. Heat fluxes through roofs and their relevance to estimates of urban heat storage. Energy Build 2009;41(7):745-752. [Google Scholar] [CrossRef]|
|64.||Sun T, Wang ZH, Ni GH. Revisiting the hysteresis effect in surface energy budgets. Geophys Res Lett 2013;40(9):1741-1747. [Google Scholar] [CrossRef]|
|65.||Grimmond CSB, Oke TR. Heat storage in urban areas: Local-scale observations and evaluation of a simple model. J Appl Meteorol 1999;38(7):922-940. [Google Scholar] [CrossRef]|
|66.||Roberts SM, Oke TR, Grimmond CSB, Voogt JA. Comparison of four methods to estimate urban heat storage. J Appl Meteorol Climatol 2006;45(12):1766-1781. [Google Scholar] [CrossRef]|
|67.||Stewart JB. Modelling surface conductance of pine forest. Agric For Meteorol 1988;43(1):19-35. [Google Scholar] [CrossRef]|
|68.||Ogink-Hendriks MJ. Modelling surface conductance and transpiration of an oak forest in The Netherlands. Agric For Meteorol 1995;74(1-2):99-118. [Google Scholar] [CrossRef]|
|69.||Krinner G, Viovy N, de Noblet-Ducoudré N, Ogée J, Polcher J, Friedlingstein P, et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochem Cycles 2005;19(1). [Google Scholar] [CrossRef]|
|70.||Best MJ, Pryor M, Clark DB, Rooney GG, Essery RLH, Ménard CB, et al. The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes. Geosci Model Dev 2011;4(3):677-699. [Google Scholar] [CrossRef]|
|71.||De Kauwe MG, Kala J, Lin YS, Pitman AJ, Medlyn BE, Duursma RA, et al. A test of an optimal stomatal conductance scheme within the CABLE land surface model. Geosci Model Dev 2015;8(2):431-452. [Google Scholar] [CrossRef]|
|72.||Jarvis PG. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philos Trans R Soc Lond B Biol Sci 1976;273(927):593-610. [Google Scholar] [CrossRef]|
|73.||Chrysoulakis N, Grimmond S, Feigenwinter C, Lindber F, Gastellu-Etchegorry JP, Marconcini M, et al. Urban energy exchanges monitoring from space. Sci Rep 2018. [Google Scholar] [CrossRef]|
|74.||Christen A, Grimmond CSB, Roth M, Pardyjak E. The IAUC Urban Flux Network An international network of micrometeorological flux towers. December. Eos Trans AGU 2009;90(52). [Google Scholar]|
|75.||Arnfield J. Köppen climate classification. Encyclopedia Britannica 2017. [Google Scholar]|
|76.||Stagakis S, Chrysoulakis N, Spyridakis N, Feigenwinter C, Vogt R. Eddy Covariance measurements and source partitioning of CO2 emissions in an urban environment: Application for Heraklion, Greece. Atmospheric Environment 2019;201:278-292. [Google Scholar] [CrossRef]|
|77.||Marconcini M, Heldens W, Del Frate F, Latini D, Mitraka Z, Lindberg F. EO-based products in support of urban heat fluxes estimation. 2017 Joint Urban Remote Sensing Event (JURSE) 2017. [Google Scholar] [CrossRef]|
|78.||Lindberg F, Grimmond CSB, Gabey A, Huang B, Kent CW, Sun T, et al. Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. Environ Model Softw 2018;99:70-87. [Google Scholar] [CrossRef]|
|79.||Sun T, Järvi L, Omidvar H, Theeuwes N, Lindberg F, Li Z, Grimmond S. Urban-Meteorology-Reading/SUEWS: 2019a Release. [Google Scholar] [CrossRef]|
|80.||Stagakis S, Burud I, Thiis T, Gaitani N, Panagiotakis E, Lantzanakis G, et al. Spatiotemporal monitoring of surface temperature in an urban area using UAV imaging and tower-mounted radiometer measurements. 2019 Joint Urban Remote Sensing Event (JURSE) 2019. [Google Scholar] [CrossRef]|
|81.||Kotthaus S, Grimmond CSB. Energy exchange in a dense urban environment - Part II: Impact of spatial heterogeneity of the surface. Urban Clim 2014;10:281-307. [Google Scholar] [CrossRef]|