Cities represent a key space for a sustainable society in
a changing environment, and our society is steadily embracing urban green
space for its role in mitigating heat waves and anthropogenic CO2
emissions. This study reports 2 years of surface fluxes of energy and
CO2 in an artificially constructed urban forest measured by the eddy
covariance method to examine the impact of urban forests on air temperature
and net CO2 exchange. The urban forest site shows typical seasonal
patterns of forest canopies with the seasonal march of the East Asian summer
monsoon. This study shows that the urban forest reduces both the warming
trend and urban heat island intensity compared to the adjacent high-rise
urban areas and that photosynthetic carbon uptake is large despite
relatively small tree density and leaf area index. During the significant
drought period in the second year, gross primary production and
evapotranspiration decreased, but their reduction was not as significant as
those in natural forest canopies. We speculate that forest management
practices, such as artificial irrigation and fertilization, enhance
vegetation activity. Further analysis reveals that ecosystem respiration in
urban forests is more pronounced than for typical natural forests in a
similar climate zone. This can be attributed to the substantial amount of
soil organic carbon due to intensive historical soil use and soil
transplantation during forest construction, as well as relatively warmer
temperatures in urban heat domes. Our findings suggest the need for caution
in soil management when aiming to reduce CO2 emissions in urban areas.
Introduction
Cities make up only 2 % of the Earth's land surface but hold more than
55 % of the world's population. It is expected that the urban population
will reach 68 % by 2050 (UN, 2019). With the unprecedented rapid
urbanization in the last century, human civilization heavily depends on
urban structures and functions. Current concern is regarding the disastrous
impacts of climatic events (e.g., heat waves, flooding, and drought) and
environmental changes (e.g., air pollution and land degradation) on our
socioeconomic system in a changing climate (McCarthy et al., 2010; Rahmstorf
and Coumou, 2011). Accordingly, it remains an urgent issue to implement
integrated policies for climate change mitigation and adaption toward
sustainable cities against global warming and related natural disasters.
Urban green infrastructures, such as urban forests, have been recognized as
a key solution toward alleviating climatic and environmental disasters
(e.g., Chiesura, 2004; Haaland and van den Bosch, 2015; Oke et al., 2017;
Kroeger et al., 2018). Green spaces in cities are exposed to wide ranges of
environmental and climatic conditions across geographical locations.
Especially when green spaces replace gray infrastructures during urban
redevelopment, it remains unclear whether their benefits emerge in real
conditions and thereby overcome their maintenance cost and other harmful
effects (e.g., allergy and ozone increase). To leverage their full potential
benefits, it is necessary to assess the biophysical effects of urban forests
based on direct long-term monitoring in urban areas.
Urban forests are a key part of green infrastructures in a city, and two of
their benefits, which have been addressed in previous studies, are thermal
mitigation and carbon uptake (Roy et al., 2012; Oke et al., 2017). Firstly,
urban forests mitigate direct sunlight and diminish the incoming radiant
energy on the land surface, thereby reducing surface temperature.
Additionally, urban forests supply water to the atmosphere through
transpiration and retain water for longer than the impervious surfaces of
urban structures. These processes contribute to reducing air temperature by
partitioning more available energy to latent heat flux (QE) than
sensible heat flux (QH), thus creating favorable conditions for
mitigating heat waves and related health problems (e.g., Oke, 1982; Hong et
al., 2019a). Eventually, this cooling effect reduces the electrical energy
load by air conditioning as well as greenhouse gas emissions. Previous
studies have reported cooling effects of urban forests at scales from street
trees to parks (Oke, 1989; Bowler et al., 2010; Norton et al., 2015;
Shashua-Bar and Hoffman, 2000). Such cooling effects depend not only on tree
species and structures (Feyisa et al., 2014) but also on the size and
vegetation density of urban green areas (Yu and Hien, 2006; Chang et al.,
2007; Hamada and Ohta, 2010; Feyisa et al., 2014). However, despite the
strong temperature-controlling factors of evapotranspiration (ET) and
sensible heat fluxes over urban forest canopies, only a few studies have
reported on surface energy balance (SEB) in urban forests in relation to
thermal mitigation based on direct measurements (e.g., Oke, 1989;
Spronken-Smith et al., 2000; Coutts et al., 2007a; Ballinas and Barradas,
2016; Hong and Hong, 2016). Moreover, it is noticeable that forest cooling
intensity depends on geography and forests can even produce a warming trend
as a result of their low albedo (Bonan, 2008; Wang et al., 2018). The lack
of direct urban forest measurements hinders proper assessment of their
influences on the climate and environment.
Furthermore, urban forests mitigate anthropogenic carbon emissions by
photosynthetic CO2 uptake. Traditionally, carbon uptake by urban
forests has been estimated by empirical relationships (e.g., biomass
allometric equation) or short-term inventory of biomass data and vegetation
growth rates, which have limitations of spatiotemporal coverage (Rowntree
and Nowak, 1991; Nowak, 1993; Nowak et al., 2008; Weissert et al., 2014).
Currently, the eddy covariance (EC) method is being applied in various
ecosystems from grasslands and natural forests to urban areas because it
provides continuous net CO2 flux measurements at the neighborhood scale
every half hour (Christen, 2014). From this perspective, the EC method is
useful for studying the net CO2 exchange (FC) from diurnal to
interannual variations, with its simultaneous measurement of surface energy
fluxes. Recently, direct FC measurements have been performed using the
EC method in urban green spaces to examine turbulent exchanges of energy and
carbon (Coutts et al., 2007a, b; Awal et al., 2010; Kordowski and
Kuttler, 2010; Bergeron and Strachan, 2011; Crawford et al., 2011; Peters
and McFadden, 2012; Velasco et al., 2013; Ward et al., 2013; Ueyama and
Ando, 2016; Hong et al., 2019b). However, the EC method provides only the
net effects of CO2 exchange from various carbon sources and sinks,
which limits the physical interpretation and assessment of the benefits and
costs of urban forests. It is challenging to partition FC into
individual sources and sinks in urban areas because of the complex
contributions from biogenic (e.g., vegetation photosynthesis, respiration of
vegetation, soil, and humans) and extra anthropogenic (e.g., fossil fuel
combustion by transportation or in households and commercial buildings)
processes (Pataki et al., 2003).
With this background, the objectives of this study include (1) reporting
temporal changes in air temperature after the artificial construction of an
urban forest park in the Seoul Metropolitan Area with a hot and humid summer
and cold and dry winter seasons and (2) quantifying the carbon uptake of
urban forests based on partitioning of FC data measured by the eddy
covariance method and meteorological data (Lee et al., 2021a). Here, we
highlight the biotic and abiotic factors controlling the carbon cycle in
urban forests and the impact of urban forests on the thermal environment
after forest park construction.
Materials and methodsUrban surface energy and CO2 balances
The SEB is expressed as
Q∗+QF=QH+QE+ΔQS+ΔQA,
where Q∗ is the net all-wave radiation of the sum of outgoing and incoming
short- and long-wave radiative fluxes, QF is the anthropogenic heat
flux, QH is the turbulent sensible heat flux, QE is the latent heat
flux, ΔQS is the net storage heat flux, and ΔQA is the
net heat advection (definitions of variables in Appendix A).
The surface CO2 budget in an urban forest is formulated as follows:
FC=ER+EB+RE-GPP≡ER+EB+NBE,
where FC is the net CO2 exchange at the city–atmosphere interface, and ER and EB are the anthropogenic CO2 emissions from fossil fuel combustion by vehicles and heating in a building, respectively. GPP and RE are biotic contributions to FC; GPP is the gross primary production by photosynthetic CO2 uptake, and RE is the ecosystem respiration. Urban
ecosystem respiration considers not only the autotrophic and heterotrophic
respirations of vegetation and soil but also human respiration (Moriwaki and
Kanda, 2004; Velasco and Roth, 2010; Ward et al., 2013, 2015; Hong et al.,
2020). Human respiration by park visitors is negligible with 0.4 µmol m2 s-1 at most.
Additionally, NBE is the net biome CO2 exchange and is typically defined
as the net ecosystem exchange (NEE) by RE - GPP for natural vegetation. Put differently, NBE
refers to carbon losses in heterotrophic respiration minus the net primary
production on natural vegetative surfaces; thus, negative NBE indicates the net
carbon uptake by the natural ecosystem (Kirschbaum et al., 2001; Randerson
et al., 2002). Unlike natural ecosystems, the FC between an urban forest and atmosphere is a complex mixture of biogenic (i.e., GPP and RE) and
anthropogenic (i.e., ER and EB) processes across various spatial and temporal scales. In urban environments, anthropogenic emissions depend on
the local characteristics (e.g., transport options, fuel types, heating
demand, climate, population density, levels of industrial activity, and
existing carbon intensity of electricity supply) of the city (Feigenwinter
et al., 2012; Kennedy et al., 2014; Lietzke et al., 2015; Stagakis et al.,
2019).
Site descriptionSeoul Forest Park
Micrometeorological measurements were taken at the Seoul Forest Park (SFP)
in the Seoul Metropolitan Area, Korea (37.5446∘ N,
127.0379∘ E). SFP is the third largest park in Seoul with an area
of 1.16 km2 (Fig. 1a). This area had been used as a horse racetrack and
a golf course inside the track since 1950 and was surrounded by cement
factories to the west (Fig. 1b). The local government initially planned this
area as a commercial district with a high-rise multi-purpose building
complex but changed its plan to redevelop the area as a green space in the late
1990s. The construction of the SFP began in December 2003, and it was opened
to the public in June 2005 (Fig. 1c).
The mean tree height (hc) is approximately 7.5 m and ranges between
5.8–9.5 m. Analysis and estimation of roughness elements and integral
turbulence characteristics are reported in Kent et al. (2018), and here we
explain the key information on the values from Macdonald (Macdonald et al.,
1998) and Kanda (Kanda et al., 2013) methods with vegetation in Kent et al. (2017). One-meter horizontal resolution digital terrain and digital surface model data are analyzed for roughness parameters and tree heights. The mean
roughness length (z0) and zero-plane displacement height (zd)
range between 0.3–0.6 and 4.1–8.2 m with wind directions, respectively.
z0 and zd have seasonal and directional variations depending on the variability of the leaves on the vegetation (Lee, 2015; Kent et al.,
2018). z0 and zd change from approximately 0.6 and 5.0 m during
leaf-on period (June–August) to 1.2 and 3.0 m during the leaf-off periods
(December–February).
Approximately 80 % of the footprint area of the SFP tower is within 250 m
(Fig. 1e), and the dominant land cover within this range is a deciduous
forest with irrigated grass lawns (Zoysia), oak (Quercus acutissima), ginkgo (Ginkgo biloba), and ash trees
(Fraxinus rhynchophylla), which correspond to the local climate zone (LCZ) “A”, dense trees
(Stewart and Oke, 2012). The maximum leaf area index (LAI) of
300 × 300 m2 around the SFP tower is approximately 1.6 (Copernicus Service
information, 2020). On the east side (0–120∘), there are trees
(approximately 230 stems ha-1) with a small artificial lake and
grasslands beyond it. Trees mainly occupy the south and west sectors of a
tower (120–330∘) within a 100 m radius area (approximately 540 stems ha-1), and traffic roads lie outside of the park (Fig. 1f).
The measurement system was installed on the rooftop of the SFP facility
building (Fig. 1d). A three-dimensional sonic anemometer (CSAT3A, Campbell
Scientific, USA) and enclosed infrared gas analyzer (EC155, Campbell
Scientific, USA) were mounted 12.2 m above the ground level (2.8 m above the
roof of an 8.4 m high building) in June 2013 for 2 years (Fig. 1d). The eddy
covariance data were recorded using the data logger (CR3000, Campbell
Scientific, USA) with a 10 Hz sampling rate and a 30 min averaging time. The
gas analyzer was calibrated with standard CO2 gas every 3 months.
The measurement height (zm) satisfied the tower height requirement over
forested or more structurally complex ecosystems in most of wind directions
(i.e., zm≅zd+4(hc-zd)), and turbulent flow is in the skimming flow region (Grimmond and Oke, 1999; Munger et al.,
2012; Kent et al., 2018). Turbulent flow can be in the wake regime in the
west direction (210–330∘) during the non-growing season (Grimmond and
Oke, 1999). Two radiometers (NR Lite2 and CMP3, Kipp & Zonen,
Netherlands) were used to measure the radiative fluxes. An auxiliary
measurement included a humidity and temperature probe (HMP155A, Vaisala,
Finland) and EVI (enhanced vegetation index) by in situ LED sensors.
The roads consist of 8 and 10 lanes carrying heavy traffic throughout
the day (∼ 100 000 vehicles d-1) to the south and west
of the tower (Fig. 1c). Hourly traffic volume, which is used for surface
flux partitioning, is evaluated on the road adjacent to the SFP tower every
year by the Seoul Metropolitan Government (https://topis.seoul.go.kr, last access: 14 January 2020).
Across the road on the western side of the tower, a cement factory still
exists, although its size is smaller than it used to be in the past (Fig. 1b
and c).
Climate conditions
Climatic condition shows a distinct seasonal variation with the seasonal
march of the East Asian summer monsoon (Fig. 2). The mean climatological
values (1981–2010) of the screen-level air temperature (Tair) and
precipitation were 12.5 ∘C and 1450 mm yr-1, respectively.
During the study period (June 2013–May 2015), the observed Tair was
higher than the climatological mean. Higher temperatures lasted longer in
the summer of 2013 with the stagnation of the migratory anticyclones (June)
and North Pacific anticyclone (July–August). There were strong heat waves in
the spring seasons of 2014 and 2015 (Hong et al., 2019a). Wind direction
also shows seasonal variation with the monsoon system. Prevailing wind is
southwesterly in spring and summer and changes to northeasterly in autumn
and northwesterly in winter (Fig. 3). Main wind comes from vegetative
surface in the park, but other land cover types are included differently
with seasons. Accordingly, road fraction in the flux footprint is larger in
spring and summer, and building emission is included only in the winter season with
northeasterly wind (Figs. 1f and 2).
Climatic conditions of the SFP for 2 years from June 2013 to May
2015: 30 min (gray dots) and daily mean (black dots) (a) air temperature with 30-year normal values of Seoul (daily mean, solid line; min and max, dashed lines), (b) vapor pressure deficit
(VPD) and missing data existing on December 2013, (c) downward shortwave radiation (K↓) and monthly averaged sunshine duration per day (black line), and (d) monthly precipitation (gray bars) and yearly accumulated precipitation (solid line).
Wind roses with seasons: (a) spring, (b) summer, (c) autumn, and (d) winter.
Notably, seasonal precipitation shows a contrasting pattern between two
consecutive years (Fig. 2d). In the first year (June 2013–May 2014), annual
precipitation was 1256 mm, which corresponded to approximately 90 % of the
climatological mean. In addition, approximately 50 % of the annual
rainfall was concentrated in the summer with an estimated 650 mm occurring
only in July 2013; however, in the second year the annual rainfall was 932 mm (i.e., 67 % of the climatological mean) (i.e., the smallest annual
precipitation in the past 20 years). The monthly precipitation values in
July and August of 2014 were 198 and 169 mm, respectively, which represented
only approximately 35 % of the climate mean. Accordingly, the vapor
pressure deficit (VPD) and downward shortwave radiation (K↓) in
July 2013 were relatively smaller than those in July 2014 (Figs. 3b and c).
Observations in the Seoul Metropolitan Area
Meteorological data from six stations (one eddy covariance station, one
aerodrome meteorological observation station, and four automatic weather
stations) in the Seoul Metropolitan Area are analyzed to examine the heat
mitigation and CO2 reduction effects of urban vegetation in the SFP
(Table 1 and Fig. 1a). The Eunpyeong eddy covariance site (EP;
37.6350∘ N, 126.9287∘ E) is for surface flux
observations in the northwest of Seoul, where there was a recent urban
redevelopment to high-rise and high-population residential areas from
low-rise areas (Hong and Hong, 2016; Hong et al., 2019b). Flux observations
at the site have been conducted since 2012, and they show the surface energy
fluxes and turbulence characteristics of a typical urban residential area.
Because the area around the SFP was originally planned to be redeveloped to
high-rise and high-population residential buildings, EP is selected for
comparative analysis as an antipodal place for the SFP region because the
sites are close to each other and so have the similar synoptic conditions.
Details of the stations used in this study.
SitesLocationLocal climate zoneMeasurementheight (m)Eddy covariance station SFP (Seoul Forest Park)37.5446∘ N, 127.0379∘ EDense tree (LCZA)12.2EP (Eunpyeong)37.6350∘ N, 126.9287∘ ECompact high-rise (LCZ1)30Weather station SD (Seongdong)37.5472∘ N, 127.0389∘ EOpen mid-rise and scatted25trees (LCZ5B)CBDCompact mid-rise and (Gangnam)37.5134∘ N, 127.0467∘ Ehigh-rise (LCZ21)20 (Seocho)37.4889∘ N, 127.0156∘ ECompact high-rise and13 (Songpa)37.5115∘ N, 127.0967∘ Emid-rise open (LCZ15)43GP (Gimpo)37.5722∘ N, 126.7751∘ ELow plants (LCZD)1.5
The Gimpo Airport weather station (GP; 37.5722∘ N,
126.7751∘ E) is located on the western boundary of Seoul, and it
is surrounded by grasslands and croplands, which corresponds to LCZ “D”. As
the dominant wind comes from the west, the GP site is generally affected by
the same synoptic weather conditions as Seoul. The GP station represents the
rural environment of the Seoul Metropolitan Area because urban development
is restricted around the airport. In this study, we select the GP site as a
reference point and calculate the urban heat island intensity (UHIi) as the
synchronous difference in Tair between the urban and rural areas
accordingly (Stewart, 2011).
The Seongdong weather station (SD; 37.5472∘ N, 127.0389∘ E), the closest station to the SFP, is located approximately 300 m north of
the SFP tower (Fig. 1c). Since the station began observations in August
2000, the meteorological data at SD are useful for analyzing temperature
changes before and after the construction of the SFP. Accordingly, it is
used to analyze local climatic changes caused by the SFP. Moreover, SD
provides auxiliary weather variables (e.g., precipitation) that are not
observed in SFP station and reference data for the gap filling.
The Gangnam, Seocho, and Songpa weather stations (hereafter denoted as CBD)
are located in Seoul's central business district, which corresponds to LCZ 1
or 2. These sites are also close to the SFP (∼ 5 km); thus,
temperatures in these regions can be assumed to be exposed to the same
synoptic condition. The annual mean maximum UHIi of CBD ranges from 3.7 to 5 ∘C and is similar to that of the SD. These regions show greater
UHIi than other parts of Seoul because of dense skyscrapers (Hong et al.,
2013, 2019a). The average temperature of these three automatic
weather stations is used to evaluate the temperature and UHIi reduction
effects of the SFP construction. All meteorological data from the automatic
weather station and aerodrome meteorological observation station are
observed every minute, and they are averaged for 1 h for UHIi analysis. All
the meteorological data are processed for quality control on the National
Climate Data Portal of the Korea Meteorological Administration
(http://data.kma.go.kr, last access: 2 December 2019).
Data processing procedures
Turbulent fluxes are computed using EddyPro (6.2.0 version, LI-COR), with
the applications of the double rotation, time lag compensation using
covariance maximization, quality test, and spectral corrections (Hong et
al., 2020, and references therein). We apply the following post processes for
quality control: (1) plausible value check, (2) spike removal, and (3) discarding the negative FC flux during the nighttime (i.e., no photosynthesis at night) (Hong et al., 2020). Negative nocturnal FC occurs occasionally (n= 485), and its accumulated value is 1.4 % of the total FC. The total study period from installation (31 May 2013) to termination (3 June 2015) is approximately 2 years (35 174 potential
30 min data), and in December 2013, there was a gap for approximately 4 weeks due to the power system failure. The total available data are
approximately 90.1 %, 88.3 %, and 85.4 % (n= 31 709, 31 064, and 30 028) for QH, QE, and FC, respectively.
The flux partitioning and gap filling methods are well documented in
previous studies of Lee et al. (2021a) and Hong et al. (2019b), and here we
describe the core of the methods. Missing values in turbulent exchange of
energy and CO2 are filled with an artificial neural network (ANN) of a
backpropagation algorithm. The ANN uses the cosine transformed
time of the day and day of the year, air temperature, relative humidity,
wind speed and direction, atmospheric pressure, precipitation, downward
shortwave radiation, cloud cover, soil temperature, and EVI.
Flux partitioning into photosynthesis and ecosystem respiration from the EC
measured FC requires additional information and data processing (e.g., Stoy et al., 2006). Stochastic FC partitioning methods were recently
applied by reprocessing EC observation data with auxiliary data and provided
useful knowledge on the carbon cycle (Hiller et al., 2011; Crawford and
Christen, 2015; Menzer and McFadden, 2017; Stagakis et al., 2019). Here we
partition the measured FC into four contributing components (i.e., RE, GPP, ER, and EB in Eq. 2) to investigate their biotic and abiotic
controlling factors in an artificially constructed park. Menzer and McFadden (2017) estimates anthropogenic emissions with traffic volume and air
temperature in winter with wind directions when anthropogenic emission is
dominant in net CO2 fluxes. This study extends the statistical
partitioning method by Menzer and McFadden (2017). Similar to Menzer and
McFadden (2017), our partitioning method chooses temporal subsets so that
some components in Eq. (2) are insignificant with footprint-weighted road
fraction so that the statistical partitioning is applicable even when
ER is not negligible. In this way, RE is estimated as a function of temperature first, and GPP is finally estimated after modeling ER and EB based on the traffic volume and high-resolution footprint-weighted
road fraction (see Fig. 1a and Table 1 in Lee et al., 2021a). Our
estimations on anthropogenic emission from vehicle and building show good
correlation with inventory data such as visitor counts, traffic volume, and
natural gas consumption in the park. More information and relevant figures
on the flux partitioning are available in Lee et al. (2021a).
Results and discussionSurface energy fluxes
Surface energy fluxes at the SFP show typical seasonal variations over
natural forest canopies with the seasonal march of the East Asian monsoon
(Fig. 4) (Hong and Kim, 2011; Hong et al., 2019b, 2020). There
are lengthy rainy spells and large temporal variabilities of meteorological
conditions during the East Asian summer monsoon period (Fig. 2d). This heavy
rainfall causes substantial decreases in K↓ and thus Q∗, with large temporal variations, thereby leading to the mid-summer depression of surface fluxes (Figs. 2c and 4). Q∗ also reaches its maximum in spring rather
than in summer and decreases gradually from spring to winter (Fig. 4). The
annual ration of QE to Q∗ at the SFP is smaller than its global average of
0.55 and values over forest canopies at similar latitudes in the East Asia
(Falge et al., 2001; Suyker and Verma, 2008; Khatun et al., 2011). In
summer, about 50 % of Q∗ is partitioned to QE, and QH is minimum
because of the ample water supply from the summer rainfall. QH is
maximum in spring and even larger in winter, despite the relatively smaller
Q∗, because of the cold and dry climatic conditions induced by the winter monsoon. Accordingly, the seasonal mean Bowen ratio β=∑QH/∑QE ranges from near zero (summer) to approximately 4
(winter), with its daily maximum around 9 in early January 2015 (Fig. 5). β in the SFP is consistently lower than the high-rise,
high-density residential area (i.e., the EP site) because of the ET from the
vegetative canopies and the unpaved surfaces in the urban forest. Daytime
Bowen ratio in summer is about 0.6, which is smaller in other urban sites
but is similar to suburban sites of the similar vegetation cover mainly
because of the small fraction of impervious spaces around the SFP station
(Table 2).
Diurnal variations of surface energy fluxes. Seasonal median
diurnal variations (points) and interquartile ranges (shaded) of 30 min downward shortwave radiation (K↓), net radiation (Q∗), sensible heat flux (QH), and latent heat flux (QE) for 2 years. Since the net radiation system was installed in September 2013, there was no Q∗ value in the first summer.
Daily Bowen ratio (β=∑QH/∑QE; dots), monthly Bowen ratio (lines), and gap-filled monthly evapotranspiration (ET;
bars) for 2 years (SFP; green, EP; brown).
Daytime Bowen ratio (β=QH/QE) in summer at the
SFP and other urban sites with vegetation cover fraction (λv).
Site nameβλvReferencesSFP0.560.57this studyBasel-Sperrstrasse2.50.16Christen and Vogt (2004)Basel-Spalenring2.30.32Christen and Vogt (2004)Tucson1.80.42Grimmond and Oke (1995)Sacramento1.40.42Grimmond and Oke (1995)Chicago0.80.44Grimmond and Oke (1995)Los Angeles1.40.41Grimmond and Oke (1995)Kansas City0.480.58Balogun et al. (2009)Oberhausen-suburban0.360.69Goldbach and Kuttler (2013)
Surface energy fluxes also shows annual variabilities influenced by the
timing of the onset and duration of the summer monsoon, similarly to natural
forest in East Asia (Hong and Kim, 2011) (Figs. 3, 4, and 5). As discussed
in Sect. 2.2.2, annual precipitation is much larger in the first year than
in the second year because of the interannual variations in the East Asian
summer monsoon activity, thereby making substantial differences in surface
radiative fluxes. Furthermore, QE shows the difference between the first
and second years of the observation, particularly by responding to such
interannual variability of radiation. In the first year, QE is more than
300 W m-2 and has a relatively larger temporal variability because of
the frequent rainfall events in summer, compared to the second year.
However, it is notable that interannual variability of surface fluxes is
relatively weaker than natural forest in this region, which will be better
manifested in ET and its ratio to precipitation.
Gap-filled annual budgets for surface energy fluxes and
precipitation (P).
ETQHQEQ∗P(mm)(MJ m-2)(MJ m-2)(MJ m-2)(mm)First year (June 2013–May 2014)36772689617971256Second year (June 2014–May 2015)3208677811848932Mean annual sum of 2 years34479783918231094
Evapotranspiration rate, which is equivalent to QE, ranges from 5 mm
per month in January 2015 to 74 mm per month in August 2013, and the
annual ET values are 367 and 320 mm yr-1 in the first and second
years, respectively (Figs. 3 and 5 and Table 3). The ET values correspond to
29.3 % and 34.3 % of the annual precipitations and 49 % and 42 % of
net radiation, respectively. The annual ET in the second year is smaller
than that in the first year, with extensive drought in the second year. The
difference in ET between the 2 consecutive years (i.e., 48 mm) mainly
occurred in summer (42 mm), especially in August (30 mm) (Fig. 5). However,
the ET in the second year shows only an approximately 12 % decrease,
despite a substantial decrease in precipitation (26 % decrease) and the
similar net radiation in the second year, compared to the first year (Table 3). Although the summer monsoon provides ample water to the ecosystem, its
delay and weakness result in severe drought and stress to the ecosystem in
this region (Hong and Kim, 2011); however, such ecosystem stress, such as
the shrinking of ET and carbon uptake, has not been extensively investigated
for the urban forest. We speculate that artificial irrigation by a sprinkler
mitigated ecosystem stress to a certain degree in the urban forest.
Urban heat island intensity
The influence of urban forests on summer temperature is evident in UHIi.
Apparently, the UHIi of the SFP (UHIiS hereafter) and CBD (UHIiC
hereafter) gradually increases after mid-afternoon and is the largest at
night (Fig. 6). This diurnal pattern is consistent with previous reports in
cities exposed to different geographical and climatic conditions because
rural areas cool faster than urban areas (Oke et al.,
2017). Additionally, UHIiC is positive throughout all days
ranging from 0.2–2.2 ∘C (i.e., warmer than rural area, GP) and
is greater than UHIiS by 0–1.5 ∘C. The reason for this
stronger UHIiC is that the CBD stations are in the central business
district; thus, the densities of buildings surrounding these stations are
much higher than those surrounding the SFP station. At night (19:00–06:00 KST),
UHIiC and UHIS are approximately 1.8 and 1.4 ∘C, respectively. The maximum UHIi difference between the CBD and
SFP was 0.7 ∘C in 2013 and 0.5 ∘C in 2014.
Hourly mean diurnal variation of the urban heat island intensity
(UHIi) of the SFP and CBD in the summer of 2013 and 2014. The error bars
represent standard errors.
Around sunrise, sharp declines in the UHIi are observed because the air
temperature near the urban area increases relatively slowly as urban
fabrics, such as asphalt, brick, and concrete, have larger heat capacities
and lower sky view factors than the rural areas (Oke et al., 2017).
Eventually, this slow increase in the air temperature reduces the
differences in Tair among the stations, thereby reducing the UHIi. The minimum UHIiC values were 0.3 ∘C (2013) at 09:30 KST and 0.2 ∘C (2014) at 08:30 KST, while the minimum UHIiS occurs at 10:30 KST
with values of -0.1 ∘C (2013) and 0.0 ∘C (2014). This
implies that the timing of the minimum UHIi is delayed in the SFP compared
to the CBD. Notably, when there is strong ET (i.e., the first year) and more
time is required to warm the SFP surface, the urban–rural difference in
thermal admittance becomes relatively small. This can be attributed to the
higher thermal capacity of the wetter soil of the SFP because of artificial
irrigation and the absence of impervious surfaces (Oke et al., 1991). The
diurnal variations in UHIiS also show the interannual variability in both amplitude and steepness over the 2 consecutive years. Despite the
similar summertime UHIiC for both years, the daytime UHIiS in 2013
was approximately 0.2 ∘C lower than that in 2014. Notably, the
summer QE was greater in 2013 than in 2014, and this observed summertime
asymmetric difference between the SFP and CBD stations was not found in the
winter when ET was negligible (not shown here).
Mean diurnal pattern of air temperature difference (ΔTair) between CBD and SD in summer (a) before and (b) after the construction of the park. CBD indicates an average of three automatic weather stations (Gangnam, Seocho, Songpa) in Seoul. The red dash line indicates the mean ΔTair before and after the construction of the park.
ΔTair is always positive during the entire summer season (i.e.,
CBD is warmer than SD) and shows distinct impacts on magnitude and diurnal
variability after the park construction (Fig. 7). This difference will be
larger if we consider that the measurement height at the CBD is higher than
that at the SD (Table 1). Notably, this temperature contrast mainly occurs
in the afternoon when ET is dominant. The maximum ΔTair is
approximately 0.3 ∘C around 10:00 KST before the park construction
(Fig. 7a) and increases up to approximately 0.5 ∘C, with its peak
occurrence shifting from the morning to the afternoon (i.e., around 14:00 KST)
after the construction (Fig. 7b). This peak time in the afternoon is
coincident with the time when photosynthesis and QE are highest. The
annual mean of the maximum UHIi in the SD is about 4 ∘C and does
not change significantly after the park construction compared to the CBD
regions (Hong et al., 2019a). On the contrary, the daytime maximum UHIi of
the SD in summer decreases after the park construction (not shown here). Our
results indicate that the thermal mitigation of the urban forest is
important because of the wetter soil surface of the park and subsequent
increases in QE, compared to the impervious surfaces in urban areas.
This is especially true if we consider that the SFP area was originally
planned to be developed as a high-population multi-purpose building complex.
Our findings emphasize that the heat mitigation of the urban forest depends
on the ratio of QE to net radiation. Indeed, there is an evident
negative relationship between daytime QE and air temperature differences
between the SFP and CBD stations (Fig. 8). As K↓ is more
partitioned to QE, Tair of the SFP decreases more than that of the
CBD, and the maximum temperature difference is observed in the summer
season. The SFP is cooler than the CBD by up to 0.6 ∘C, but the
SFP is warmer than the CBD during the winter-dormant season when ET is
small. Our findings confirm that urban forests are responsible for
substantial changes in the thermal environment in terms of QH and
QE, as well as their related air and surface temperatures because of
more evaporative cooling in green spaces compared to impervious surfaces
such as roads and buildings in urban areas (Oke et al., 2017).
Relationship between the ratio of monthly QE to
K↓ and mean air temperature difference between SFP and CBD
during the daytime (K↓> 120 W m-2) for 2
years. The quotation and double-quotation marks on the scatter indicate the
first and second year of the observation period, respectively. The error
bars represent standard errors based on daily values, and the gray dotted
line is calculated using linear regression model considering errors in both
axes (York et al., 2004).
Temporal dynamics of net CO2 exchange
Overall, the mean daytime FC is negative (i.e., carbon uptake) in the
summer (June–August), indicating that photosynthesis, the only carbon sink,
is dominant in the growing season (Fig. 9). This carbon uptake period is
coincident with the active vegetation manifested by increases in EVI (not
shown here). Summertime photosynthetic carbon uptake (GPP) has a daily average of 7.6 µmol m-2 s-1 with a maximum of 18.9 µmol m-2 s-1 around 12:30 KST (Figs. 9 and 10). A daily minimum FC also occurs
around 12:30 KST with the maximum photosynthetic carbon uptake during this time.
CO2 uptake is highest in June, with a maximum of approximately 13 µmol m-2 s-1 (Fig. 9a). In the middle of summer (2-week data from the 4th and 31st in Fig. 9a), CO2 uptake decreases significantly because
photosynthesis is limited because of the reduced K↓ by cloud
and rainfall with the onset of the summer monsoon (Fig. 2c). This mid-summer
depression of carbon uptake has been reported in the Asian natural
vegetation (e.g., Kwon et al., 2009; Hong and Kim, 2011; Hong et al.,
2014). Greater reduction in CO2 uptake observed in 2013 than in 2014
was attributed to a longer monsoon period in 2013. Indeed, from 8 to 21 July
2013 (2-week data from the 4th in Fig. 9a), the accumulated precipitation was
approximately 400 mm for 2 weeks, and the daily averaged K↓
was only 70 W m-2.
(a) Temporal variation of hourly averaged FC and (b) footprint-weighted road fraction (λ) as every 2-week average (x axis: the date; y axis: time of day). In December 2013, there was a gap for approximately 4 weeks due to the power system failure. The yellow numbers in the x axis indicate the transition period when traffic emissions (ER) contribute to the observed FC significantly.
Monthly boxplots of daytime (K↓> 120 W m-2) FC by wind direction. Boxes have a minimum of 20 samples. Box
limits are upper and lower quartiles, and whiskers are distances of 1.5 times the interquartile range from each quartile. Median and mean values are
indicated by the black and pink horizontal lines. The average source-area-weighted road fractions (λ) are shown below the graph, and wind
sectors with λ greater than 1 % are shaded in gray.
The vegetation around the SFP absorbs more CO2 than is emitted by local
carbon sources, and FC is negative only during the summer daytime.
Because of substantial amounts of anthropogenic emissions and ecosystem
respiration, FC changes from negative (i.e., carbon sink) to positive
values (i.e., carbon source) even around 16:30 KST in summer unlike in natural
ecosystems, despite the substantial downward shortwave radiation (e.g.,
Desai et al., 2008; Hong et al., 2009; Alekseychik et al., 2017; Chatterjee
et al., 2020). As photosynthesis decreases, FC changes to positive
values from November. During the non-growing season (i.e., late autumn,
winter, and early spring), anthropogenic emissions were also dominant
because photosynthesis and ecosystem respiration decrease with smaller K↓ and lower temperatures. During these periods, FC had minimum values
at 04:00–05:00 KST and increases until 15:00–16:00 KST. The diurnal variations in
FC mainly followed the traffic volume. There also is a clear positive relationship between FC and λ (Fig. 4 in Lee et al., 2021a). It
is also noteworthy that the peak time of FC (16:00 KST) is earlier than the
peak time of λ (18:00 KST) from December to early March because
EB is the largest at around 15:00–16:00 KST, indicating that ER and EB are the controlling factor of FC in this period.
The seasonal FC variation also depends on the spatiotemporal
distribution of CO2 sources and flux footprint because the latter
covers various land use with changes in wind direction and atmospheric
stability (Fig. 10). In autumn, the main wind direction changes to the north
as the synoptic conditions change as discussed in Sect. 2 (Fig. 3);
therefore, λ is smaller in autumn compared to other seasons (Fig. 9b). For example, the road fraction is smallest at < 1 % from
midnight to midday and < 3 % during the afternoon in October and
November (2-week data from the 11th, 12th, 36th, and 37th in Fig. 9b). In these
periods, the nighttime FC shows the lowest value of approximately 2.9 µmol m-2 s-1, which is attributable to the smallest road
fraction, lower respiration, and minimal heating usage.
In early spring, λ is generally larger; thus, ER plays a
significant role in FC, and EB remains non-zero until early April
because of anthropogenic emission by hot water and space heating in the
building within the footprint, thereby resulting in the largest FC in this period. With a shutdown of the heating system (i.e., zero EB) and
the sprouting of leaves in April, there is a sharp decrease in FC (Fig. 10b). From December to March, CO2 emissions increase up to 30 µmol m-2 s-1 with larger variability because of intermittent
anthropogenic emissions from the park facility building in the southwest
directions (due to space heating and boiling water), as well as the
relatively increased contribution of vehicles on the road in the western
part of the site (Fig. 10b).
Although the positive FC in the winter decreases in spring, its
magnitude shows directional differences (Fig. 10b). On the eastern side,
the mean FC shows a negative value in May, whereas it remains positive on
the western side (210–270∘) until May. These findings further
indicate the different contributions of various carbon sources and sinks
among the different wind directions. For the wind directions from the north
to the east (0–120∘), FC shows a relatively weaker carbon
sink than other directions because of the relatively low tree fraction in
this direction (Fig. 10). On the southern side (150–180∘) having
the highest tree cover fraction, a maximum carbon uptake is about 15 µmol m-2 s-1 in June. However, despite the dense vegetation on the south and west side (120–330∘), the FC magnitude was much
smaller than that of other natural forests. This is related to the
anthropogenic emissions from vehicles on the roads, which is discussed in
Sect. 3.5.
Light use efficiency of biogenic CO2 components
FC at the SFP shows a typical light response to the photosynthetically
active radiation (PAR) in a way similar to natural ecosystems in spite of
anthropogenic CO2 sources from vehicles (Fig. 11). However, this light
response in the urban forest is a distinct contrast to FC in high-rise,
high-population residential areas in Seoul under the same climatic
conditions that does not respond to PAR (i.e., EP station). Importantly,
GPP, NBE, and FC show different trends with PAR depending on the direction. As stated in Sects. 2.2.1 and 3.3, the western side has a higher density of
trees as against more grass on the eastern side, and biotic CO2 uptake
from the western side is substantially larger than that on the eastern side.
Accordingly, the slope of the light-response curve for PAR on the western
side is steeper than on the eastern side. FC at zero PAR
(FC_0) is larger on the western side (9.7 µmol m-2 s-1) than on the eastern side (5.1 µmol m-2 s-1) because of a contribution of ER from roads on the western side of the tower.
During the growing season (June–August 2013, 2014) when
EB is negligible, light-response curves as a function of
photosynthetically active radiation (PAR, in bins of 100 µmol m-2 s-1): (a) for the western sectors (150∘<Φ< 300∘) and (b) for the eastern sectors (30∘<Φ< 90∘). Black
line is a rectangular hyperbolic equation fitting net biome exchange (NBE = RE - GPP =FC-ER) to PAR, and EP (brown line) is a light-response curve for the
high-rise, high-population residential area in Seoul. The shaded areas
indicate interquartile range.
NBE shows a comparable light response to natural vegetation (e.g., Schmid et
al., 2003). A rectangular hyperbolic equation has been used to examine the
light response of NBE and elucidate the directional differences in carbon
uptake:
NBE=-GPP+RE=-α⋅GPPsat⋅PARGPPsat+α⋅PAR+RE.α is approximately 0.0651 and 0.0558 µmol CO2 (µmol photon)-1 on the western and eastern sides, respectively. Notably,
α on the western side is comparable to the high initial quantum
yield in crops and subtropical forests in East Asia (Hong et al., 2019b;
Emmel et al., 2020). Additionally, GPPsat is 30.9 and 12.7 µmol m-2 s-1 on the western and eastern sides, respectively. In addition, the light saturation points are at a PAR of 1500 µmol m-2 s-1 on the eastern side, which occur at a relatively lower PAR than on the
western side. Daytime respiration estimates from Eq. (3) are 6.7 and 6.3 µmol m-2 s-1 on the western and eastern sides, respectively. Because GPP is related to PAR, the difference in monthly cumulative GPP between
the 2 years shows a close relationship with the difference in the monthly
sunshine duration (r2=0.75, not shown here), suggesting a possible
impact of change in the onset of the summer monsoon on urban forests.
The magnitude of NBE from the western side is larger than that from a suburban area with about 50 % vegetative fraction in Montreal, Canada (Fig. 7b in Bergeron and Strachan, 2011), and FC from a highly vegetated environment
of about 67 % vegetative fraction in Baltimore, USA (Crawford et al.,
2011). Also, GPP from the western side is comparable to the dense forest
canopies in subtropical forests in Korea (Hong et al., 2019b), deciduous
forest ecosystems (Goulden et al., 1996), and a mixed hardwood forest
ecosystem (Schmid et al., 2000). However, NBE from the eastern side is similar to FC from the suburban areas of about 44 %, 50 %, and 64 %
vegetative fraction in Swindon, UK (Ward et al., 2013); Montreal, Canada
(Bergeron and Strachan, 2011); and Ochang, Korea, in the same climate zone
(Hong et al., 2019b), respectively.
Annual budget of CO2 sources and sink
The annual sums of the GPP and RE in the SFP are 4.6 kg CO2 m-2 yr-1 (1244 g C m-2 yr-1) and 5.1 kg CO2 m-2 yr-1 (1378 g C m-2 yr-1), respectively (Table 4). This
photosynthetic carbon uptake is smaller than its global mean GPP in natural
deciduous broadleaf forests with similar annual precipitation and annual
mean air temperature (total 8 years of data from four sites of the FLUXNET2015
dataset reported in Pastorello et al., 2020) and similar to that of
deciduous broadleaf forests in East Asia (Awal et al., 2010; Kwon et al.,
2009) (Table 5). However, we note that this GPP is relatively larger if we
consider the low vegetation fraction and leaf area index (LAI) at our urban
park. Previous studies have shown that the GPP of urban vegetation is scaled
with vegetation cover fraction with an increase of about 0.7 kg CO2 m-2 yr-1 per 10 % increase in vegetation cover fraction (Awal
et al., 2010; Crawford and Christen, 2015; Velasco et al., 2016; Menzer and
McFadden, 2017). Indeed, GPP at the SFP with a 46.6 % vegetation cover
fraction is approximately 1.5 kg CO2 m-2 yr-1, which is
larger than values reported in other urban sites if it is scaled with the
vegetation cover fraction (Fig. 12a).
Gap-filled annual budgets for FC (observed by EC measurement) and its components, indicating ecosystem respiration (RE), photosynthetic uptake by vegetation (GPP), vehicle emissions (ER), and building emissions (EB). All fluxes are in kg CO2 m-2 yr-1.
SitesFCREGPPEREBFirst year (June 2013–May 2014)6.65.1 (77 %)4.7 (70 %)5.4 (81 %)1.0 (15 %)Second year (June 2014–May 2015)7.65.0 (65 %)4.5 (59 %)5.4 (71 %)1.9 (25 %)Mean annual sum of 2 years7.15.1 (71 %)4.6 (64 %)5.4 (76 %)1.5 (20 %)
Annual budgets of biogenic FC components and ratios in
deciduous broadleaf forests in similar climatic conditions reported in
previous studies. All fluxes are in kg CO2 m-2 yr-1.
Site nameReferenceMATMAPmaximumREGPPNBERE / GPP(∘)(mm)LAISeoul Forest ParkThis study13.910941.65.14.6+0.51.11Nagoya urban forestAwal et al. (2010)15.916805.54.96.2-1.30.74Toyota rural forest14.515184.52.64.6-2.00.56Gwangneung deciduous forestKwon et al. (2009)12.8148753.84.1-0.30.93Kiryu Experimental WatershedTakanashi et al. (2005)14.113095.53.95.6-1.70.70FLUXNET2015 dataset∗Pastorello et al. (2020)14.511134.16.0-1.90.68
∗ Average value of 8-year data from four sites having mean annual temperature (MAT) of 12–16∘ and mean annual precipitation (MAP) of 900–2000 mm.
Relationship between vegetation fraction (a) annual GPP and (b) annual FC in urban sites. Dashed line in panel (a) and (b) indicates a linear
regression of GPP in urban sites from Awal et al. (2010), Crawford and
Christen (2015), Velasco et al. (2016), and Menzer and McFadden (2017) as well as
NEE from Hong et al. (2019b) and references therein scaled with vegetation
fraction, respectively. See main texts for more information.
Despite this larger GPP resulting in smaller FC eventually, there is no substantial decrease in FC when they are scaled by vegetation fraction, suggesting large contribution of RE (Fig. 12b). There was a linear decrease in FC of approximately 3.0 kg CO2 m-2 yr-1 per 10 % increase in
vegetation cover fraction based on the observed FC across an
urbanization gradient (Hong et al., 2019b, and references therein). The
annual FC in the SFP is not so much different from other similar cities and this scaled relationship. Meanwhile, RE at our site is much larger than
that in natural temperate deciduous forests in the similar climate zone
(Takanashi et al., 2005; Kwon et al., 2009) and similar to that in the urban
forest in East Asia (Awal et al., 2010), as well as to the global mean RE over
forests with similar annual precipitation and annual mean air temperatures
(Pastorello et al., 2020). Put differently, the urban forest considered in
our study is an outlier compared to other natural forest canopies and urban
forests because RE / GPP > 1 (Table 5). Autotrophic respiration is
considered to be approximately half of GPP as a rule of thumb (Piao et al.,
2010), which corresponds to approximately 45 % of the RE at our site,
thereby indicating a large contribution of heterotrophic respiration to
RE. Indeed, it was reported that soil respiration at the same site was
approximately 4 kg CO2 m-2 yr-1 (Bae and Ryu, 2017). The
reason for the large soil organic carbon was mainly because rice cultivation
was carried out in this region before the 1950s, organic carbon-rich
soil was transplanted during the SFP construction, and fertilizers were
applied regularly. It has also been reported that RE is enhanced in urban areas
because of the relatively warmer temperature in urban regions (i.e., UHI)
(Awal et al., 2010). Notably, Q10 (the rate by which respiration is
multiplied when temperature increases by 10 ∘C) is about 1.9 at
the site and matches the Q10 value for ecosystem respiration
(2.2 ± 0.7) calculated for natural forests across 42 FLUXNET sites (Mahecha
et al., 2010). Further analysis based on the observed Q10 and the UHIi
at the SFP indicates that UHI leads to an approximately 5 % increase in
RE.
Monthly sums for gap-filled FC (yellow bar) with RE (red bar), ER (blue bar), EB (gray bar), and -GPP (green bar).
Seasonal variations in the strength of carbon sources and sink as well as
FC are mainly regulated by the biogenic component in summer and the anthropogenic component in winter (Fig. 13). Furthermore, FC is minimum in June, despite the similar GPP from June to August because of the relatively
smaller RE during the summer season. Even in summer, photosynthetic carbon
uptake is balanced with ecosystem respiration and does not offset all biotic
and anthropogenic emissions, thus resulting in positive FC values
throughout the year. In winter, EB is dominant, with negligible GPP and RE due to cold temperatures, and ER also becomes larger than RE from November. ER shows apparent seasonal variation in wind direction and atmospheric stability. Its magnitude is about 0.0666 µmol m-2 per vehicle half hour per second in neutral condition and consistent with the value in the inventory
data (Lee et al., 2021a). The average monthly traffic speed for the road in
front of the SFP is 50–60 km h-1 (based on the January 2014 data from
the Seoul Metropolitan Government Traffic Speed Report), and the CO2
emission rate is approximately 0.15 kg CO2 km-1 per vehicle based on
the emission data at this speed (Kim et al., 2011). With the width of the
10-lane road (25–30 m), the inventory-based slope (i.e., CO2 emission
rate per vehicle per area per half hour) is approximately in the range of
0.0631–0.0757 µmol m-2 per vehicle half hour per second
(≅ 150 g CO2 km-1 per vehicle ×1/30 or
1/25 m-1×1/44 mol g CO2-1×10-3 km m-1×106µmol mol-1×1/1800
half hour s-1).
There is an evident yearly difference in individual carbon sources and sink
in 2 consecutive years. EB is mainly caused by heating buildings and hot water in park facilities using natural gas. Notably, EB is highly
correlated with gas consumption in SFP during winter on a monthly basis
(R2=0.94; Fig. 6 in Lee et al., 2021a). EB is smaller in the
first year because of the relatively smaller number of park visitors and
consequently smaller gas consumption, compared to the second year.
Eventually, these annual differences lead to a smaller annual mean totalFC
in the first year than in the second year (Table 4). RE is maximum in
August of the first year, while it is highest in July in the second year
because the monthly mean air temperature is highest in August of the first year
and July of the second year, with annual variations in air temperature with
changes in the timing and duration of the East Asian summer monsoon, of
which impacts have also been reported in natural vegetation in the same
region (Hong and Kim, 2011; Hong et al., 2019b). GPP in summer is relatively
smaller in the first year due to the mid-summer depression of solar radiation
because of the elongated monsoon period, but annual sums of GPP are similar
in 2 years (Table 4 and Fig. 13). GPP does not shrink in the second year of
significant drought because of ample water supply by a sprinkler.
Eventually, FC in the SFP is approximately 3.0 kg CO2 m-2 yr-1 less than that in recently developed high-rise, high-population
urban areas in Seoul. Our results suggest that efficient management of urban
forests, such as regular irrigation and fertilization, can be an efficient
way to adapt and mitigate climate change by increasing CO2 uptake in
artificial forest constructions in East Asia.
Summary and conclusions
This study reported 2-year surface fluxes of energy and CO2 measured
by the eddy covariance method in order to examine the role of artificially
generated urban forests in mitigating air temperature and anthropogenic
CO2 emissions. The study area is an urban park with an artificially
planted forest in the Seoul Metropolitan Area redeveloped from a racetrack
and factory in the mid-2000s, where it is influenced by a lengthy summer rainy
season during the East Asian summer monsoon. To examine the mitigation of
air temperature, this study compares meteorological conditions in the urban
forest with the surrounding high-rise, high-population urban areas. This
study applies for the ANN-based gap filling (Hong et al., 2019b; Lee et al.,
2021a) and a statistical CO2 flux partitioning method (Lee et al., 2021a)
based on temporal subsets of flux data and high-resolution
footprint-weighted land use data to understand the abiotic and biotic
contributions to FC.
Surface energy fluxes in the SFP are influenced by the summer monsoon, and
more energy is distributed to QE than QH in the summer in the growing
season, similarly to natural forests in this climate zone. The Bowen ratio
in this urban forest ranges from near 0 (summer) to about 4 (winter), which
is lower throughout the year than that of high-rise and high-density
residential areas in Seoul. This suggests that the vegetation and unpaved
surfaces of urban forests facilitate more evaporative cooling compared to
the impervious surfaces in urban areas. During the measurement period, the
second year is contrasted with the first year because of the drought
compared to the normal climate condition in the first year. Notably, ET
decreases in the second year, but this drop is not as much as the reduced
precipitation and its related changes in radiative forcing during the
drought because of the artificial irrigation by a sprinkler-mitigated
ecosystem.
It is also evident that the urban forest reduced the warming trend and UHIi
around the study area. Air temperature in the SFP is lower than the
surrounding area, but this coolness is reinforced after the park was
created. The warming trend diminishes after the construction of the park and
is smaller than that in other urban regions in the Seoul Metropolitan Area.
In addition, the construction of the park delays the timing of the maximum
temperature difference between the urban forest and high-rise commercial buildings
from the morning to the afternoon, coinciding with the timing of the maximum
QE. The SFP shows a typical diurnal UHIi variation pattern, which has a
higher temperature at night than in rural areas. However, the UHIi in SFP is
lower by 0.6 ∘C in summer compared to the surrounding urban area,
and the time of the minimum peak time is delayed, possibly because
vegetation and permeable soils in SFP have a larger thermal capacity.
Notably, UHIi decreases more in the partitioning of incoming energy into
latent heat fluxes, and there was cooling by 0.2 ∘C compared to
the surrounding urban area if QE/K↓ increased by 10 % in
this study.
Net CO2 exchange at the urban forest shows typical temporal
variations in natural forest canopies influenced by the East Asian summer
monsoon. A mid-summer depression of carbon uptake is observed with the onset
of the summer monsoon, like vegetation in the East Asian monsoon region. The
GPP is estimated by the statistical partitioning method, and the non-zero GPP period is coincident with the active vegetation of the significant vegetation
index. Summertime photosynthetic carbon uptake has a daily average of 7.6 µmol m-2 s-1 with a maximum of 18.9 µmol m-2 s-1 around 12:30 KST. However, even during the growing season, vegetative
carbon uptake is insufficient to offset anthropogenic CO2 emissions and
ecosystem respiration on a timescale of > 1 d. Our estimations
of anthropogenic CO2 emissions from vehicles and buildings agree with
the estimations based on inventory data such as CO2 emission rate of
vehicles and monthly gas consumption, and their annual budgets each have a
comparable magnitude to GPP.
Annual GPP of the urban forest is relatively smaller than that of the forest in
East Asia exposed to similar climatic conditions because of the relatively
smaller vegetation cover fraction and LAI. However, it is larger than the GPP expected from the relationship from previous urban studies if it is normalized by the vegetation cover fraction. RE is, however, much larger than that in the temperate
East Asian forests and is similar to the urban forest in East Asia. We
speculate that soil respiration enhances such large RE by relatively warmer
temperatures in a city and rich soil organic carbon in the SFP. The annual
mean total FC is 7.1 kg CO2 m-2 yr-1, which is smaller
than the estimate from the scaling between annual total FC and
vegetation fraction (Hong et al., 2019b). Because of the spatial
heterogeneity, FC and its components showed directional changes. NBE from
the eastern side is similar to that in suburban areas with approximately
44 %, 50 %, and 64 % vegetative fraction in Swindon, UK (Ward et al.,
2013); Montreal, Canada (Bergeron and Strachan, 2011); and Ochang, Korea
in the same climate zone (Hong et al., 2019b), respectively. However, the
NBE and GPP from the western side are comparable to dense forest canopies in
subtropical forests in Korea (Hong et al., 2019b), deciduous forest
ecosystems (Goulden et al., 1996), and a mixed hardwood forest ecosystem
(Schmid et al., 2000).
Our results emphasize the important role of forest management in enhancing
carbon uptake and evaporative cooling despite the low vegetation fraction.
Our key findings are that urban forests in East Asia are highly influenced
by the East Asian monsoon like natural forests in this region, but such
influence is mitigated by artificial irrigation and fertilization in urban
forests. Our results emphasize the importance of forest management for
efficient carbon uptake and evaporative cooling despite the low vegetation
fraction. Furthermore, our observation study also indicates that caution in
soil management is necessary to reduce CO2 emissions in urban forests,
mainly resulting from large soil organic carbon and a warm environment.
List of abbreviation
AbbreviationDefinitionsCBDthe Gangnam, Seocho, and Songpa observatories at central business districtEBCO2 emission from buildingsECeddy covarianceEPthe Eunpyeong siteERCO2 emission from vehicles on roadsETevapotranspirationEVIenhanced vegetation indexFCnet CO2 exchangeFC_0FC at zero PARGPthe Gimpo weather stationGPPgross primary productionGPPsatpotential rate of ecosystem CO2 uptakeK↓downward shortwave radiationLCZlocal climate zoneMAPmean annual precipitationMATmean annual temperatureNBEnet biome exchange of CO2 (RE - GPP)PprecipitationPARphotosynthetic active radiationQElatent heat fluxQFanthropogenic heat fluxQHsensible heat fluxQ∗net radiationQ10the rate by which respiration is multiplied when temperature increases by 10∘REecosystem respirationSDthe Seongdong weather stationSEBsurface energy balanceSFPthe Seoul Forest ParkTairthe screen-level air temperatureTair_CBDair temperature at the CBD regionsTair_SDair temperature at the SDUHIurban heat islandUHIiurban heat island intensityUHIiCUHIi at CBDUHIiSUHIi at SFPVPDvapor pressure deficitΔTairTair_CBD-Tair_SDΔQSthe net storage heat fluxΔQAthe net heat advectionhcmean canopy heightz0mean roughness lengthzdzero-plane displacement heightzmmeasurement heightαquantum yield efficiencyβBowen ratio =∑QH/∑QEλsource-area-weighted road ratioλvvegetation cover fraction
Code and data availability
All data and codes are available in Lee et al. (2021b, 10.22647/EAPL-SFP_202101) and upon request to the corresponding author (jhong@yonsei.ac.kr, https://eapl.yonsei.ac.kr, last access: 4 December 2021).
Author contributions
KL, JWH, and JH contributed to the observation, experimental design, data analysis, and manuscript preparation. JK and SJ contributed to the writing and data interpretation.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Our thanks go to the editor and reviewers for their constructive comments to improve our study.
Financial support
This research has been supported by the Korea Meteorological Administration Research and Development Program (grant no. KMI2021-01610).
Review statement
This paper was edited by Christoph Gerbig and reviewed by two anonymous referees.
ReferencesAlekseychik, P., Mammarella, I., Karpov, D., Dengel, S., Terentieva, I., Sabrekov, A., Glagolev, M., and Lapshina, E.: Net ecosystem exchange and energy fluxes measured with the eddy covariance technique in a western Siberian bog, Atmos. Chem. Phys., 17, 9333–9345, 10.5194/acp-17-9333-2017, 2017.
Awal, M. A., Ohta, T., Matsumoto, K., Toba, T., Daikoku, K., Hattori, S., Hiyama, T., and Park, H.: Comparing the carbon sequestration capacity of temperate
deciduous forests between urban and rural landscapes in central Japan,
Urban For. Urban Gree., 9, 261–270, 2010.
Bae, J. and Ryu, Y.: Spatial and temporal variations in soil respiration
among different land cover types under wet and dry years in an urban park,
Landscape Urban Plan., 167, 378–385, 2017.
Ballinas, M. and Barradas, V. L.: The urban tree as a tool to mitigate the
urban heat island in Mexico City: A simple phenomenological model,
J. Environ. Qual., 45, 157–166, 2016.
Balogun, A. A., Adegoke, J. O., Vezhapparambu, S., Mauder, M., McFadden, J.
P., and Gallo, K.: Surface energy balance measurements above an exurban
residential neighbourhood of Kansas City, Missouri,
Bound.-Lay. Meteorol., 133, 299–321, 2009.Bergeron, O. and Strachan, I. B.: CO2 sources and sinks in urban and
suburban areas of a northern mid-latitude city, Atmos. Environ.,
45, 1564–1573, 2011.
Bonan, G. B.: Forests and climate change: forcings, feedbacks, and the
climate benefits of forests, Science, 320, 1444–1449, 2008.
Bowler, D. E., Buyung-Ali, L., Knight, T. M., and Pullin, A. S.: Urban
greening to cool towns and cities: A systematic review of the empirical
evidence, Landscape Urban Plan., 97, 147–155, 2010.
Chang, C. R., Li, M. H., and Chang, S. D.: A preliminary study on the local
cool-island intensity of Taipei city parks, Landscape Urban Plan.,
80, 386–395, 2007.Chatterjee, S., Swain, C. K., Nayak, A. K., Chatterjee, D., Bhattacharyya, P.,
Mahapatra, S. S., Debnath, M., Tripathi, R., Guru, P. K., and Dhal, B.:
Partitioning of eddy covariance-measured net ecosystem exchange of CO2
in tropical lowland paddy, Paddy Water Environ., 18, 623–636, 2020.
Chiesura, A.: The role of urban parks for the sustainable city, Landscape Urban Plan., 68, 129–138, 2004.
Christen, A.: Atmospheric measurement techniques to quantify greenhouse gas
emissions from cities, Urban Climate, 10, 241–260, 2014.
Christen, A. and Vogt, R.: Energy and radiation balance of a central
European city, Int. J. Climatol., 24, 1395–1421, 2004.Copernicus Service information: Copernicus Global Land Service Site, available at: https://land.copernicus.eu/global/, last access: 10 April 2020.
Coutts, A. M., Beringer, J., and Tapper, N. J.: Impact of increasing urban
density on local climate: Spatial and temporal variations in the surface
energy balance in Melbourne, Australia, J. Appl. Meteorol.
Clim., 46, 477–493, 2007a.Coutts, A. M., Beringer, J., and Tapper, N. J.: Characteristics influencing
the variability of urban CO2 fluxes in Melbourne, Australia,
Atmos. Environ., 41, 51–62, 2007b.Crawford, B. and Christen, A.: Spatial source attribution of measured urban
eddy covariance CO2 fluxes, Theor. Appl. Climatol.,
119, 733–755, 2015.
Crawford, B., Grimmond, C. S. B., and Christen, A.: Five years of carbon
dioxide fluxes measurements in a highly vegetated suburban area, Atmos. Environ., 45, 896–905, 2011.
Desai, A. R., Richardson, A. D., Moffat, A. M., Kattge, J., Hollinger, D. Y.,
Barr, A., Falge, E., Noormets, A., Papale, D., Reichstein, M., and Stauch,
V. J.: Cross-site evaluation of eddy covariance GPP and RE decomposition
techniques, Agr. Forest Meteorol., 148, 821–838, 2008.
Emmel, C., D'Odorico, P., Revill, A., Hörtnagl, L., Ammann, C.,
Buchmann, N., and Eugster, W.: Canopy photosynthesis of six major arable
crops is enhanced under diffuse light due to canopy architecture, Glob.
Change Biol., 26, 5164–5177, 2020.
Falge, E., Baldocchi, D., Olson, R., Anthoni, P., Aubinet, M., Bernhofer,
C., Burba, G., Ceulemans, R., Clement, R., Dolman, H., Granier, A., Gross,
P., Grünwald, T., Hollinger, D., Jensen, N., Katul, G., Keronen, P.,
Kwalski, A., Lai, C., Law, B., Meyers, T., Moncrieff, J., Moors, E., Munger,
W., Pilegaard, K., Rannik, Ü., Rebmann, C., Suyker, A., Tenhunen, J.,
Tu, K., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: Gap filling
strategies for long term energy flux data sets, Agr. Forest Meteorol., 107, 71–77, 2001.
Feigenwinter, C., Vogt, R., and Christen, A.: Eddy covariance measurements
over urban areas, in: Eddy Covariance, Springer, Dordrecht, the Netherlands, 377–397, 2012.
Feyisa, G. L., Dons, K., and Meilby, H.: Efficiency of parks in mitigating
urban heat island effect: An example from Addis Ababa, Landscape Urban Plan., 123, 87–95, 2014.
Goldbach, A. and Kuttler, W.: Quantification of turbulent heat fluxes for
adaptation strategies within urban planning, Int. J. Climatol., 33, 143–159, 2013.
Goulden, M. L., Munger, J. W., Fan, S. M., Daube, B. C., and Wofsy, S. C.:
Measurements of carbon sequestration by long-term eddy covariance: Methods
and a critical evaluation of accuracy, Glob. Change Biol., 2, 169–182,
1996.
Grimmond, C. S. B. and Oke, T. R.: Comparison of heat fluxes from summertime
observations in the suburbs of four North American cities, J.
Appl. Meteorol., 34, 873–889, 1995.
Grimmond, C. S. B. and Oke, T. R.: Aerodynamic properties of urban areas
derived from analysis of surface form, J. Appl. Meteorol.
Clim., 38, 1262–1292, 1999.
Haaland, C. and van Den Bosch, C. K.: Challenges and strategies for urban
green-space planning in cities undergoing densification: A review, Urban
For. Urban Gree., 14, 760–771, 2015.
Hamada, S. and Ohta, T.: Seasonal variations in the cooling effect of urban
green areas on surrounding urban areas, Urban For. Urban Gree.,
9, 15–24, 2010.Hiller, R. V., McFadden, J. P., and Kljun, N.: Interpreting CO2 fluxes
over a suburban lawn: the influence of traffic emissions, Bound.-Lay. Meteorol., 138, 215–230, 2011.
Hong, J. and Kim, J.: Impact of the Asian monsoon climate on ecosystem
carbon and water exchanges: a wavelet analysis and its ecosystem modeling
implications, Glob. Change Biol., 17, 1900–1916, 2011.
Hong, J., Kwon, H., Lim, J., Byun, Y., Lee, J., and Kim, J.: Standardization
of KoFlux eddy-covariance data processing, Korean J. Agric. For. Meteorol.,
11, 19–26, 2009.
Hong, J., Takagi, K., Ohta, T., and Kodama, Y.: Wet surface resistance of
forest canopy in monsoon Asia: Implications for eddy-covariance measurement
of evapotranspiration, Hydrol. Process., 28, 37–42, 2014.
Hong, J. W. and Hong, J.: Changes in the Seoul metropolitan area urban heat
environment with residential redevelopment, J. Appl. Meteorol.
Clim., 55, 1091–1106, 2016.
Hong, J. W., Hong, J., Lee, S. E., and Lee, J.: Spatial distribution of
urban heat island based on local climate zone of automatic weather station
in Seoul metropolitan area, Atmosphere, 23, 413–424, 2013.Hong, J. W., Hong, J., Kwon, E. E., and Yoon, D.: Temporal dynamics of urban
heat island correlated with the socio-economic development over the past
half-century in Seoul, Korea, Environ. Pollut., 254, 112934, 10.1016/j.envpol.2019.07.102, 2019a.Hong, J.-W., Hong, J. Chun, J., Lee, Y., Chang, L., Lee, J., Yi, K., Park,
Y., Byun, B., and Joo, S.: Comparative assessment of net CO2 exchange
across an urbanization gradient in Korea based on in situ observation,
Carbon Balance and Management, 14, 13, 10.1186/s13021-019-0128-6,
2019b.Hong, J. W., Lee, S. D., Lee, K., and Hong, J.: Seasonal variations in the
surface energy and CO2 flux over a high-rise, high-population,
residential urban area in the East Asian monsoon region, Int. J. Climatol., 40, 4384–4407, 10.1002/joc.6463, 2020.
Hsieh, C. I., Katul, G., and Chi, T. W.: An approximate analytical model for
footprint estimation of scalar fluxes in thermally stratified atmospheric
flows, Adv. Water Resour., 23, 765–772, 2000.
Kanda, M., Inagaki, A., Miyamoto, T., Gryschka, M., and Raasch, S.: A new
aerodynamic parametrization for real urban surfaces, Bound.-Lay.
Meteorol., 148, 357–377, 2013.
Kennedy, C. A., Ibrahim, N., and Hoornweg, D.: Low-carbon infrastructure
strategies for cities, Nat. Clim. Change, 4, 343–346, 2014.
Kent, C. W., Grimmond, S., and Gatey, D.: Aerodynamic roughness parameters
in cities: Inclusion of vegetation, J. Wind Eng.
Ind. Aerod., 169, 168–176, 2017.
Kent, C. W., Lee, K., Ward, H. C., Hong, J. W., Hong, J., Gatey, D., and
Grimmond, S.: Aerodynamic roughness variation with vegetation: analysis in a
suburban neighbourhood and a city park, Urban Ecosyst., 21, 227–243,
2018.
Khatun, R., Ohta, T., Kotani, A., Asanuma, J., Gamo, M., Han, S., Hirano,
T., Nakai, Y., Saigusa, N., Takagi, K., and Wang, H.: Spatial
variations in evapotranspiration over East Asian forest sites. I.
Evapotranspiration and decoupling coefficient, Hydrological Research
Letters, 5, 83–87, 2011.Kim, Y., Woo, S. K., Park, S., Kim, M., and Han, D.: A Study on Evaluation
Methodology of Greenhouse Gas and Air Pollutant Emissions on Road Network –
Focusing on Evaluation Methodology of CO2 and NOx Emissions from Road. Korea: The Korea Transport Institute, Annual Report, Sejong, Korea, 10.23000/TRKO201300014649, 2011.
Kirschbaum, M. U. F., Eamus, D., Gifford, R. M., Roxburgh, S. H., and Sands,
P. J.: Definitions of some ecological terms commonly used in carbon
accounting, in: Proceedings Net Ecosystem Exchange CRC Workshop, 18–20 April 2001, Canberra, Australia, 2–5, 2001.
Kordowski, K. and Kuttler, W.: Carbon dioxide fluxes over an urban park
area, Atmos. Environ., 44, 2722–2730, 2010.Kroeger, T., McDonald, R. I., Boucher, T., Zhang, P., and Wang, L.: Where
the people are: Current trends and future potential targeted investments in
urban trees for PM10 and temperature mitigation in 27 US cities, Landscape Urban Plan., 177, 227–240, 2018.
Kwon, H., Park, T. Y., Hong, J., Lim, J. H., and Kim, J.: Seasonality of Net
Ecosystem Carbon Exchange in Two Major Plant Functional Types in Korea,
Asia-Pac. J. Atmos. Sci., 45, 149–163, 2009.Lee, K.: Energy, water and CO2 exchanges in an artificially constructed
urban forest, Master Degree Dissertation, Yonsei University, Seoul, Korea, 2015.Lee, K., Hong, J. W., Kim, J., and Hong, J.: Partitioning of net CO2
exchanges at the city-atmosphere interface into biotic and abiotic
components, MethodsX, 8, 101231, 10.1016/j.mex.2021.101231, 2021a.Lee, K., Hong, J. W., Kim, J., and Hong, J.: CO2 flux partitioning program and example data over the urban forest, Yonsei University [data set], 10.22647/EAPL-SFP_202101, 2021b.
Lietzke, B., Vogt, R., Feigenwinter, C., and Parlow, E.: On the controlling
factors for the variability of carbon dioxide flux in a heterogeneous urban
environment, Int. J. Climatol., 35, 3921–3941, 2015.
Macdonald, R. W., Griffiths, R. F., and Hall, D. J.: An improved method for
the estimation of surface roughness of obstacle arrays, Atmos. Environ., 32, 1857–1864, 1998.
Mahecha, M. D., Reichstein, M., Carvalhais, N., Lasslop, G., Lange, H.,
Seneviratne, S. I., Vargas, R., Ammann, C., Arain, M. A., Cescatti, A.,
Janssens, I., Migliavacca, M., Montagnani, L., and Richardson, A.: Global
convergence in the temperature sensitivity of respiration at ecosystem
level, Science, 329, 838–840, 2010.McCarthy, M. P., Best, M. J., and Betts, R. A.: Climate change in cities due
to global warming and urban effects, Geophys. Res. Lett., 37, L09705, 10.1029/2010GL042845, 2010.Menzer, O. and McFadden, J. P.: Statistical partitioning of a three-year
time series of direct urban net CO2 flux measurements into biogenic and
anthropogenic components, Atmos. Environ., 170, 319–333, 2017.
Moriwaki, R. and Kanda, M.: Seasonal and diurnal fluxes of radiation, heat,
water vapor, and carbon dioxide over a suburban area, J. Appl.
Meteorol., 43, 1700–1710, 2004.
Munger, J. W., Loescher, H. W., and Luo, H.: Measurement, tower, and site
design considerations, in: Eddy Covariance, Springer, Dordrecht, the Netherlands, 21–58,
2012.
Norton, B. A., Coutts, A. M., Livesley, S. J., Harris, R. J., Hunter, A. M.,
and Williams, N. S.: Planning for cooler cities: A framework to prioritise
green infrastructure to mitigate high temperatures in urban landscapes,
Landscape Urban Plan., 134, 127–138, 2015.
Nowak, D. J.: Atmospheric carbon reduction by urban trees, J. Environ. Manage., 37, 207–217, 1993.
Nowak, D. J., Crane, D. E., Stevens, J. C., Hoehn, R. E., Walton, J. T., and
Bond, J.: A ground-based method of assessing urban forest structure and
ecosystem services, Aboriculture and Urban Forestry, 34, 347–358,
2008.
Oke, T. R.: The energetic basis of the urban heat island, Q. J. Roy. Meteor. Soc., 108, 1–24, 1982.
Oke, T. R.: The micrometeorology of the urban forest, Philos. T. Roy. Soc. B,
324, 335–349, 1989.
Oke, T. R., Johnson, G. T., Steyn, D. G., and Watson, I. D.: Simulation of
surface urban heat islands under “ideal” conditions at night part 2:
Diagnosis of causation, Bound.-Lay. Meteorol., 56, 339–358, 1991.
Oke, T. R., Mills, G., Christen, A., and Voogt, J. A.: Urban Climates, Cambridge University Press, Cambridge, UK, 2017.
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah,
Y. W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac,
P., Polidori, D., Ribeca, A., van Ingen, C., Zhang, L., Amiro, B., Ammann,
C., Arain, M. A., Ardö, J., Arkebauer, T., Arndt, S. K., Arriga, N., Aubinet, M., Aurela, M., Baldocchi, D., Barr, A., Beamesderfer, E., Marchesini, L. B., Bergeron, O., Beringer, J., Bernhofer, C., Berveiller,
D., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Boike,
J., Bolstad, P. V., Bonal, D., Bonnefond, J.-M., Bowling, D. R., Bracho,
R., Brodeur, J., Brümmer, C., Buchmann, N., Burban, B., Burns,
S. P., Buysse, P., Cale, P., Cavagna, M., Cellier, P., Chen, S., Chini,
I., Christensen, T. R., Cleverly, J., Collalti, A., Consalvo, C., Cook,
B. D., Cook, D., Coursolle, C., Cremonese, E., Curtis, P. S., D'Andrea, E., da Rocha, H., Dai, X., Davis, K. J., De Cinti, B., de Grandcourt, A., De Ligne, A., De Oliveira, R. C., Delpierre, N., Desai, A. R., Di Bella, C. M., di Tommasi, P., Dolman, H., Domingo, F., Dong, G., Dore, S., Duce,
P., Dufrêne, E., Dunn, A., Dušek, J., Eamus, D., Eichelmann,
U., ElKhidir, H. A. M., Eugster, W., Ewenz, C. M., Ewers, B., Famulari,
D., Fares, S., Feigenwinter, I., Feitz, A., Fensholt, R., Filippa,
G., Fischer, M., Frank, J., Galvagno, M., Gharun, M., Gianelle, D., Gielen,
B., Gioli, B., Gitelson, A., Goded, I., Goeckede, M., Goldstein,
A. H., Gough, C. M., Goulden, M. L., Graf, A., Griebel, A., Gruening,
C., Grünwald, T., Hammerle, A., Han, S., Han, X., Hansen, B. U., Hanson,
C., Hatakka, J., He, Y., Hehn, M., Heinesch, B., Hinko-Najera,
N., Hörtnagl, L., Hutley, L., Ibrom, A., Ikawa, H., Jackowicz-Korczynski, M., Janouš, D., Jans, W., Jassal, R., Jiang, S., Kato, T., Khomik, M., Klatt, J., Knohl, A., Knox, S., Kobayashi, H., Koerber, G., Kolle, O., Kosugi, Y., Kotani, A., Kowalski, A., Kruijt, B., Kurbatova, J., Kutsch, W. L., Kwon, H., Launiainen, S., Laurila, T., Law, B., Leuning, R., Li, Y., Liddell, M., Limousin, J.-M., Lion, M., Liska, A. J., Lohila, A., López-Ballesteros, A., López-Blanco, E., Loubet, B., Loustau, D., Lucas-Moffat, A., Lüers, J., Ma, S., Macfarlane, C., Magliulo, V., Maier, R., Mammarella, I., Manca, G., Marcolla, B., Margolis, H. A., Marras, S., Massman, W., Mastepanov, M., Matamala, R., Matthes, J. H., Mazzenga, F., McCaughey, H., McHugh, I., McMillan, A. M. S., Merbold, L., Meyer, W., Meyers, T., Miller, S. D., Minerbi, S., Moderow, U., Monson, R. K., Montagnani, L., Moore, C. E., Moors, E., Moreaux, V., Moureaux, C., Munger, J. W., Nakai, T., Neirynck, J., Nesic, Z., Nicolini, G., Noormets, A., Northwood, M., Nosetto, M., Nouvellon, Y., Novick, K., Oechel, W., Olesen, J. E., Ourcival, J.-M., Papuga, S. A., Parmentier, F.-J., Paul-Limoges, E., Pavelka, M., Peichl, M., Pendall, E., Phillips, R. P., Pilegaard, K., Pirk, N., Posse, G., Powell, T., Prasse, H., Prober, S. M., Rambal, S., Rannik, Ü., Raz-Yaseef, N., Reed, D., de Dios, V. R., Restrepo-Coupe, N., Reverter, B. R., Roland, M., Sabbatini, S., Sachs, T., Saleska, S. R., Sánchez-Ca nete, E. P., Sanchez-Mejia, Z. M., Schmid, H. P., Schmidt, M., Schneider, K., Schrader, F., Schroder, I., Scott, R. L., Sedlák, P., Serrano-Ortíz, P., Shao, C., Shi, P., Shironya, I., Siebicke, L., Šigut, L., Silberstein, R., Sirca, C., Spano, D., Steinbrecher, R., Stevens, R. M., Sturtevant, C., Suyker, A., Tagesson, T., Takanashi, S., Tang, Y., Tapper, N., Thom, J., Tiedemann, F., Tomassucci, M., Tuovinen, J.-P., Urbanski, S., Valentini, R., van der Molen, M., van Gorsel, E., van Huissteden, K., Varlagin, A., Verfaillie, J., Vesala, T., Vincke, C., Vitale, D., Vygodskaya, N., Walker, J. P., Walter-Shea, E., Wang, H., Weber, R., Westermann, S., Wille, C., Wofsy, S., Wohlfahrt, G., Wolf, S., Woodgate, W., Li, Y., Zampedri, R., Zhang, J., Zhou, G., Zona, D., Agarwal, D., Biraud, S., Torn, M., and Papale, D.: The FLUXNET2015 dataset and the ONEFlux processing
pipeline for eddy covariance data, Scientific Data, 7, 1–27, 2020.Pataki, D. E., Bowling, D. R., and Ehleringer, J. R.: Seasonal cycle of
carbon dioxide and its isotopic composition in an urban atmosphere:
Anthropogenic and biogenic effects, J. Geophys. Res.-Atmos., 108, 4735, 10.1029/2003JD003865, 2003.Peters, E. B. and McFadden, J. P.: Continuous measurements of net CO2
exchange by vegetation and soils in a suburban landscape, J.
Geophys. Res.-Biogeo., 117, G03005,
10.1029/2011JG001933, 2012.
Piao, S., Luyssaert, S., Ciais, P., Janssens, I. A., Chen, A., Cao, C.,
Fang, J., Friedlingstein, P., Luo, Y., and Wang, S.: Forest annual carbon
cost: A global-scale analysis of autotrophic respiration, Ecology, 91,
652–661, 2010.
Rahmstorf, S. and Coumou, D.: Increase of extreme events in a warming
world, P. Natl. Acad. Sci. USA, 108,
17905–17909, 2011.
Randerson, J. T., Chapin III, F. S., Harden, J. W., Neff, J. C., and Harmon,
M. E.: Net ecosystem production: a comprehensive measure of net carbon
accumulation by ecosystems, Ecol. Appl., 12, 937–947, 2002.
Rowntree, R. A. and Nowak, D. J.: Quantifying the role of urban forests in
removing atmospheric carbon dioxide, Journal of Arboriculture, 17,
269–275, 1991.
Roy, S., Byrne, J., and Pickering, C.: A systematic quantitative review of
urban tree benefits, costs, and assessment methods across cities in
different climatic zones, Urban For. Urban Gree., 11, 351–363,
2012.Schmid, H. P., Grimmond, C. S. B., Cropley, F., Offerle, B., and Su, H. B.:
Measurements of CO2 and energy fluxes over a mixed hardwood forest in
the mid-western United States, Agr. Forest Meteorol., 103,
357–374, 2000.Schmid, H. P., Su, H. B., Vogel, C. S., and Curtis, P. S.:
Ecosystem-atmosphere exchange of carbon dioxide over a mixed hardwood forest
in northern lower Michigan, J. Geophys. Res.-Atmos.,
108, 4417, 10.1029/2002JD003011, 2003.
Shashua-Bar, L. and Hoffman, M. E.: Vegetation as a climatic component in
the design of an urban street: An empirical model for predicting the cooling
effect of urban green areas with trees, Energ. Buildings, 31,
221–235, 2000.
Spronken-Smith, R. A., Oke, T. R., and Lowry, W. P.: Advection and the
surface energy balance across an irrigated urban park, Int. J. Climatol., 20,
1033–1047, 2000.Stagakis, S., Chrysoulakis, N., Spyridakis, N., Feigenwinter, C., and Vogt,
R.: Eddy Covariance measurements and source partitioning of CO2
emissions in an urban environment: Application for Heraklion, Greece,
Atmos. Environ., 201, 278–292, 2019.
Stewart, I. D.: A systematic review and scientific critique of methodology
in modern urban heat island literature, Int. J. Climatol., 31, 200–217, 2011.
Stewart, I. D. and Oke, T. R.: Local climate zones for urban temperature
studies, B. Am. Meteorol. Soc., 93, 1879–1900,
2012.
Stoy, P. C., Katul, G. G., Siqueira, M. B., Juang, J. Y., Novick, K. A.,
Uebelherr, J. M., and Oren, R.: An evaluation of models for partitioning
eddy covariance-measured net ecosystem exchange into photosynthesis and
respiration, Agr. Forest Meteorol., 141, 2–18, 2006.Suyker, A. E. and Verma, S. B.: Interannual water vapor and energy exchange in an irrigated maize-based agroecosystem, Agr. Forest Meteorol., 148, 417–427, 2008.
Takanashi, S., Kosugi, Y., Tanaka, Y., Yano, M., Katayama, T., Tanaka, H.,
and Tani, M.: CO2 exchange in a temperate Japanese cypress forest
compared with that in a cool-temperate deciduous broad-leaved forest,
Ecol. Res., 20, 313–324, 2005.Ueyama, M. and Ando, T.: Diurnal, weekly, seasonal, and spatial variabilities in carbon dioxide flux in different urban landscapes in Sakai, Japan, Atmos. Chem. Phys., 16, 14727–14740, 10.5194/acp-16-14727-2016, 2016.
United Nations (UN): World Urbanization Prospects: The 2018 Revision
(ST/ESA/SER.A/420), Department of Economic and Social Affairs, Population
Division, United Nations, New York, USA, 2019.Velasco, E. and Roth, M.: Cities as net sources of CO2: Review of
atmospheric CO2 exchange in urban environments measured by eddy
covariance technique, Geography Compass, 4, 1238–1259, 2010.Velasco, E., Roth, M., Tan, S. H., Quak, M., Nabarro, S. D. A., and Norford, L.: The role of vegetation in the CO2 flux from a tropical urban neighbourhood, Atmos. Chem. Phys., 13, 10185–10202, 10.5194/acp-13-10185-2013, 2013.
Velasco, E., Roth, M., Norford, L., and Molina, L. T.: Does urban vegetation
enhance carbon sequestration?, Landscape Urban Plan., 148, 99–107,
2016.
Wang, L., Lee, X., Schultz, N., Chen, S., Wei, Z., Fu, C., Gao, Y., Yang, Y,
and Lin, G.: Response of surface temperature to afforestation in the Kubuqi
Desert, Inner Mongolia, J. Geophys. Res.-Atmos., 123,
948–964, 2018.Ward, H. C., Evans, J. G., and Grimmond, C. S. B.: Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK, Atmos. Chem. Phys., 13, 4645–4666, 10.5194/acp-13-4645-2013, 2013.
Ward, H. C., Kotthaus, S., Grimmond, C. S. B., Bjorkegren, A., Wilkinson,
M., Morrison, W. T. J., Evans, J. G., Morison, J. I. L., and Iamarino, M.:
Effects of urban density on carbon dioxide exchanges: Observations of dense
urban, suburban and woodland areas of southern England, Environ.
Pollut., 198, 186–200, 2015.Weissert, L. F., Salmond, J. A., and Schwendenmann, L.: A review of the
current progress in quantifying the potential of urban forests to mitigate
urban CO2 emissions, Urban Climate, 8, 100–125, 2014.
York D., Evensen N., Martinez M., and Delgado J.: Unified equations for the
slope, intercept, and standard errors of the best straight line, Am.
J. Phys., 72, 367–375, 2004.
Yu, C. and Hien, W. N.: Thermal benefits of city parks, Energ.
Buildings, 38, 105–120, 2006.