Extensive green roof CO2 exchange and its seasonal variation quantified by eddy covariance measurements

Science of the Total Environment 607–608 (2017) 623–632

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Extensive green roof CO2 exchange and its seasonal variation quantified by eddy covariance measurements Jannik Heusinger ⁎, Stephan Weber Climatology and Environmental Meteorology, Institute of Geoecology, Technische Universität Braunschweig, Langer Kamp 19c, 38106 Braunschweig, Germany

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Eddy covariance applied to study extensive green roof CO2 exchange • Green roof turned in a temporary C source in summer • Annual cumulative NEE was − 85 g C m− 2 year− 1.

a r t i c l e

i n f o

Article history: Received 4 April 2017 Received in revised form 4 July 2017 Accepted 6 July 2017 Available online xxxx Editor: Elena Paoletti Keywords: CO2 uptake Dry periods A-gs model Carbon sequestration Urban

a b s t r a c t The CO2 surface-atmosphere exchange of an unirrigated, extensive green roof in Berlin, Germany was measured by means of the eddy covariance method over a full annual cycle. The present analysis focusses on the cumulative green roof net ecosystem exchange of CO2 (NEE), on its seasonal variation and on green roof physiological characteristics by applying a canopy (A-gs) model. The green roof was a carbon sink with an annual cumulative NEE of −313 g CO2 m−2 year−1, equivalent to −85 g C m−2 year−1. Three established CO2 flux gap-filling methods were applied to estimate NEE and to study the performance during different meteorological situations. A best estimate NEE time series was established, which chooses the gap filling method with the highest performance. During dry periods daytime carbon uptake was shown to decline linearly with substrate moisture below a threshold of 0.05 m3 m−3, whereas night-time respiration was unaffected by substrate moisture variation. The roof turned into a temporary C source during dry conditions in summer 2015. We conclude that the carbon uptake of the present green roof can be optimized when substrate moisture is kept above 0.05 m3 m−3. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Owing to the provisioning of different urban ecosystem services (UESS), green roofs were intensively studied during recent years. Their positive characteristics are related to UESS in fields such as thermal urban climate, urban air quality, urban water retention and biodiversity ⁎ Corresponding author. E-mail address: [email protected] (J. Heusinger).

http://dx.doi.org/10.1016/j.scitotenv.2017.07.052 0048-9697/© 2017 Elsevier B.V. All rights reserved.

(Gómez-Baggethun et al., 2013; Grunwald et al., 2017; Oberndorfer et al., 2007). Additionally, green roofs support climate change mitigation in two ways: by decreasing building energy consumption due to optimized thermal insulation (Sailor, 2008), and by uptake of CO2 via photosynthesis and sequestration of CO2 into the soil substrate via plant litter and root exudates (Getter et al., 2009; Rowe, 2011). Furthermore, green roofs can exert indirect benefits such as reducing urban thermal stress, which lessens energy consumption and in turn CO2 emissions (Berardi, 2016; Sun et al., 2016). Another asset of green roof carbon mitigation

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is that this urban greening strategy is not in space competition with the surface built environment, which is in contrast to other types of urban greening, e.g. parks and green spaces. Green roofs are distinguished into extensive and intensive types. Extensive green roofs are composed of substrate depths between 0.02 and 0.15 m (Berndtsson, 2010). The vegetation typically consists of droughtresistant Sedum species that are considered as facultative CAM (crassulaceaen acid metabolism) plants (Earnshaw et al., 1985; Starry et al., 2014). Sedum green roofs are often complemented by herbaceous plants, grasses and mosses. The extensive green roof is generally not irrigated, while the intensive types have larger substrate depths and consist of herbaceous plants, shrubs and trees which need irrigation for survival. Since the intensive green roof has much higher static requirements and overall costs, the extensive type is usually favored. In Germany N90% of the installed green roof area is comprised of the extensive, non-irrigated type (Heusinger and Weber, 2017). To assess the climate change mitigation potential of green roofs in terms of carbon sequestration, it is necessary to study the amount of CO2 uptake, i.e. the net ecosystem exchange of CO2 (NEE), preferably for a full annual cycle. The calculation of the cumulative annual NEE determines the source or sink strength of a respective study site. The stateof-the-art method to study NEE is the eddy covariance (EC) method which is currently in use at hundreds of different ecosystem sites worldwide, e.g. forests, peatlands, grasslands and urban sites (Chu et al., 2017; Fortuniak et al., 2017; Weber and Kordowski, 2010). Although the EC method was applied to analyze green roof exchange fluxes of heat and water it was not used to study green roof NEE, yet (Heusinger and Weber, 2017). Generally, only very few studies on green roof surfaceatmosphere exchange of CO2 are available. Getter et al. (2009) studied the sequestration rate of CO2 by analyzing the carbon content of the plant material and substrate of an extensive green roof. Over a period of 2 years the green roof sequestered 375 g C m− 2. A study from a Sedum covered Canadian green roof using surface chambers to quantify carbon exchange reported a net carbon uptake of 440 g C m−2 year−1 (Skabelund et al., 2015). Grasslands - which may serve for comparison purposes - are generally considered as carbon sinks, but can turn into carbon sources during dry conditions (Flanagan et al., 2002; Novick et al., 2004). However, considering the annual NEE, they show high variability in dependence on climate, geographical location, and interannual dynamics (Gilmanov et al., 2007). A similar behavior is evident for other terrestrial ecosystems such as peatlands, woodlands or forested ecosystems (van Gorsel et al., 2016; Wharton et al., 2012). Our hypothesis is that green roofs are carbon sinks on an annual basis, but exhibit temporal variation in dependence of ambient meteorological conditions. Hence, green roofs may turn into carbon sources during dry periods. Quantifications of extensive green roof NEE are necessary to analyze the overall climate change mitigation potential of green roofs. Hence, the motivation of the present study is to quantify the cumulative annual green roof NEE by using the EC method, to study its seasonal variation, to look into the NEE characteristics during dry periods, as well as to analyze physiological characteristics by means of modelling. 2. Material and methods 2.1. Green roof study site and instrumentation Turbulent surface-atmosphere exchange measurements of CO2 were conducted from 01 July 2014 to 31 August 2015 on a flat roof of a multistorey carpark located at the Berlin Brandenburg Airport (BER), Berlin, Germany. The BER airport is still under construction (mainly interior construction) and was not in operation during the measurement period, i.e. very limited traffic activity in the airport vicinity. Approximately 2.5 km north-east of BER the airport Berlin-Schönefeld (SXF) is located, which uses a runway located north of the green roof site (c.f. Section 2.3).

The extensive green roof has a size of 8600 m2. The substrate depth is 0.09 m. The green roof vegetation is mainly composed of Sedum species and herbaceous plants (dominant species are Sedum floriferum ‘Weihenstephaner Gold, Sedum album and Allium schoenoprasum’), which were not irrigated throughout the measurement period. The plant height varied between 0.1 and 0.3 m. The mean fractional cover of vegetation (σf) was 0.40 ± 0.13 (±1 standard deviation), i.e. 40% of the roof area was covered by plants (Heusinger and Weber, 2017). The EC setup consisted of an ultrasonic anemometer (CSAT3A, Campbell Scientific, USA) and an open-path infrared gas analyzer (EC150, Campbell Scientific, USA), both of which were installed at a height of 1.15 m above roof level (arl). The site was further equipped with a NR01 sensor (Hukseflux, Netherlands) to measure short- and longwave radiation (downward and upward), a HMP155 probe (Vaisala, Finland) for air temperature and relative humidity in 2 m arl, a tipping bucket (Adolf Thies, Germany) for precipitation (P), four soil temperature and moisture sensors (5TM, Decagon, USA) and a heat flux plate (HFP01SC, Hukseflux, Netherlands). Leaf area index (LAI) and vegetation coverage were monitored during a sub-period from 15 April 2015 to 31 August 2015. More details on the measurement site and green roof characteristics were reported in Heusinger and Weber (2017). To characterize local climate conditions during the study period, data from a nearby meteorological station of the German Weather Service (1.9 km north-east of the study site, Berlin Schönefeld) was used. 2.2. Data analysis The software EddyPro Version 5.1.1 was used to process the 10 Hz EC raw flux data applying 30 min block averaging, double coordinate rotation, WPL (Webb et al., 1980) and spectral corrections (Moncrieff et al., 2005, 1997). Further details were reported in Heusinger and Weber (2017). A spike detection algorithm marked 0.4% of the 30-min CO2 fluxes (75 values). Periods with precipitation and/or when a leaf wetness sensor signalled surface wetness as well as data marked with quality flags ≥ 7 (Foken et al., 2004) and flux data from wind directions 36°–54°, corresponding to N±171° of the CSAT3 azimuth angle (225°; Foken, 2016) were excluded. After quality assurance and quality control (QA/QC), 62% of CO2 flux data (FCO2) remained for further analysis. To quantify cumulative annual NEE, a continuous, gap-free time series of CO2 fluxes is necessary. Data gaps resulting from QA/QC procedures had to be gap-filled, which requires accurate methods without systematic errors that would sum up in the annual estimate (Falge et al., 2001). To ensure a robust and accurate annual estimate of NEE, three established gap-filling methods were applied: a moving look-up table algorithm with marginal distribution sampling (MDS; Reichstein et al., 2005), artificial neural networks (ANN; Järvi et al., 2012; Papale and Valentini, 2003) and mean diurnal variation (MDV; Bamberger et al., 2014). Our assumption was that each method might have specific abilities to capture FCO2 variation during certain periods of the year, certain meteorological conditions or different gap lengths. For a more detailed description of the different gap-filling methods, we refer to Appendix A. For each gap-filling method a time series depending on the meteorological input was generated for the whole measurement period. To achieve a gap-filled FCO2 time series for the calculation of annual green roof NEE, a best estimate (BE) of all three methods was selected by choosing the method with the lowest RMSE between measured and modelled FCO2 for a moving time window ±6 h centered on the ith FCO2 value (RMSEcurrent). When a gap was longer than the RMSEcurrent moving time window, the method with the lowest RMSE concerning the complete time series was used. To finally build a BE FCO2 time series, the ith FCO2 value of the method with the lowest RMSEcurrent was chosen, respectively. BE was used for quantification of green roof NEE in subsequent data analysis. Dry periods were defined according to the procedure reported in Heusinger and Weber (2017). The dependence of FCO2 on declining soil moisture as well as meteorological variables during dry periods

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was studied by linear regression. The following meteorological variables were studied: air temperature (TA; °C), vapor pressure (VP, kPa), vapor pressure deficit (VPD, hPa), global radiation (K↓; W m−2) and turbulent kinetic energy (TKE; m2 s−2). To estimate annual NEE and to study seasonal variation of FCO2, data is presented for a full annual period from 01 September 2014 to 31 August 2015 (termed study period further on). For the analysis of dry periods as well as functional relationships, data from the complete measurement period was used. 2.3. Analysis of the potential influence of aircraft and diffuse CO2 emissions on FCO2 Our previous study demonstrated that the mean 70% footprint isoline according to the Kormann and Meixner (2001) model is almost entirely within the fetch of the green roof for daytime and night-time data. Hence measured fluxes were shown to represent green roof – atmosphere exchange (Heusinger and Weber, 2017). However, point emission sources (aircrafts, vehicles) could potentially have influenced measured CO2 fluxes. This was the reason to include the 90% flux footprint in subsequent analysis. The influence of aircraft emissions on flux measurements were studied using time schedule data of arrivals and departures at the nearby airport Berlin Schönefeld (SXF; c.f. Section 3.2.1). From September 2014 to April 2015 a runway north of the green roof was under operation. In the period from May 2015 to August 2015 a southern runway was used alternatively, because of construction works at the northern runway. The wind sectors that were potentially influenced by the northern and southern runways comprise 272° to 37° (W to NE; 768 m distance to sensor at closest point) and 144° to 231° (SE to SW; 1141 m distance to closest point), respectively (Fig. S1). We assumed that airplane emission plumes would lead to notable CO2 peaks and an increase of variance in FCO2. The time span between an emission event and plume arrival at the sensor was considered by dividing the distance between runway and sensor (m) by wind speed (m s−1). Then the time span was rounded up to the next full 30 min time interval. The FCO2 value that was used to study the dependence on the number of arrivals and departures per 30 min was calculated by taking the arithmetic mean FCO2 of the arrival time as well as the previous and following 30 min value. Since the footprint size is considerably larger at night-time, we focused on nocturnal FCO2 data to study the dependence on aircraft emissions. The influence of diffuse emissions from vehicles and other sources was studied in dependence on wind direction and day of week. Since the airport was not in operation during the study period besides construction and maintenance works, we expected lower emissions on Sunday compared to weekdays. Additionally, we analyzed data when the 90% footprint isoline was closer than 100 m to the runways. The distance between sensor and runways was calculated in dependence of the sonic anemometer azimuth angle. 2.4. Green roof physiological characterization In order to study the physiological characteristics driving the surfaceatmosphere exchange of the extensive green roof plant community, a semi-empirical A-gs model was used (Jacobs, 1994). It calculates the photosynthesis rate and net assimilation of CO2 (A) depending on atmospheric and substrate boundary conditions. The input variables are K↓ (W m−2), TA (°C), air pressure (Pa), specific humidity (g kg−1), saturation deficit (g kg−1), CO2 concentration (ppm), volumetric water content of the substrate (VWC) and LAI. The following brief description of the model will focus on aspects that were modified in contrast to the original model. These changes were made in order to vary stomatal reactions due to changing substrate moisture, parameterize canopy variation of photosynthetic active radiation (PAR) and to upscale FCO2 from the leaf to the green roof canopy scale.

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Reduced water availability was accounted for according to the ISBAA-gs model (Calvet et al., 1998): γ¼

VWC−VWCwilt VWCFC −VWCwilt

ð1Þ

where VWCwilt (m3 m−3) is the permanent wilting point (0.01), VWCFC (m3 m−3) is substrate moisture at field capacity (0.3) and γ is used as a reduction coefficient for mesophyll conductance (gm; mg m−2 s−1). The canopy CO2 assimilation is reduced compared to single leaf assimilation by the reduction of photosynthetic active radiation (PAR; W m−2) inside the canopy. PAR was parameterized by a more simplified approach in comparison to Jacobs (1994). However, this was assumed sufficient given the low canopy height of the green roof vegetation. PAR was estimated by (Szeicz, 1974): PAR ¼ 0:5  K↓

ð2Þ

In-canopy variation of PAR was accounted for by calculating a mean PAR value from top to bottom of the canopy (PARcanopy): PARcanopy ¼

PARTop þ PARBottom 2

ð3Þ

where PARTop is incident PAR at the top of the canopy and PARBottom is PAR at the substrate surface. The reduction of PAR through the canopy was assumed to be exponential according to the Beer-Lambert law (Pierce and Running, 1988): PARBottom ¼ PARTop  expð−k  LAIÞ

ð4Þ

where k is the extinction coefficient (0.4) and LAI is the leaf area index (m2 m−2). The extinction coefficient was assumed to be lower for Sedum and grass vegetation (e.g. Thornley and France, 2007) compared to values which are given for forests (Pierce and Running, 1988). The respiration was modelled according to Reichstein et al. (2005), which is based on fitting the exponential regression model after Lloyd and Taylor (1994) for 15 day subperiods. Measured LAI values were used during the vegetation period, where available, according to Heusinger and Weber (2017). During other periods a sigmoid curve was fitted to account for LAI seasonal variation (c.f. Supplementary materials, Fig. S2). The surface-atmosphere exchange of CO2 was upscaled from leaf (FCO2 Ags) to the green roof scale (FCO2 GR) by: FCO2 GR ¼ σ f  LAI  FCO2 Ags

ð5Þ

Table 1 Variation of air temperature (TA) and precipitation (P) during the study period (SP) and mean variation for the 1981–2010 climate normal period (CN) for Berlin-Schönefeld (Heusinger and Weber, 2017). Deviations for P of N50% compared to the climate normal sum are marked in bold letters.

Sep 2014 Oct 2014 Nov 2014 Dec 2014 Jan 2015 Feb 2015 Mar 2015 Apr 2015 May 2015 Jun 2015 Jul 2015 Aug 2015 Annual mean (TA); sum (P)

TA SP (°C)

TA LT (°C)

ΔTA (°C)

P SP (mm)

P CN (mm)

ΔP (mm)

ΔP (%)

16.2 12.4 6.8 2.6 2.9 1.6 5.9 9.5 13.8 17.0 20.4 22.4 10.9

14.2 9.4 4.4 1.0 0.1 1.0 4.3 9.0 14.0 16.8 19.1 18.5 9.3

2.0 3.0 2.4 1.6 2.8 0.6 1.6 0.5 −0.2 0.2 1.3 3.9 1.6

40.5 33.5 5.8 43.8 72.7 7.2 29.9 17 14.8 38.6 63.3 24.1 391.2

42.1 33.7 40.4 44.4 38.9 30.7 38.2 31.0 55.4 58.1 58.0 54.5 525.2

−1.6 −0.2 −34.6 −0.6 33.8 −23.5 −8.3 −14 −40.6 −19.5 5.3 −30.4 −134

96 99 14 99 187 24 78 55 27 66 109 44 75

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Fig. 1. Global radiation (W m−2), air temperature (°C) and daily precipitation sums (mm) during the study period.

where σf is 0.4 (Heusinger and Weber, 2017). This means that a linear relationship between the canopy CO2 exchange and σf multiplied by LAI was assumed. Jacobs (1994) reports a set of physiological parameter values for C3 and C4 plants (Table S1 c.f. Supplementary materials). To study the physiological characteristics of the (bulk) green roof vegetation, a new parameter set was estimated. The new set of parameters was found by applying a genetic algorithm for optimization in Matlab and gradually increasing the lower and upper parameter constraints from ± 1% to ± 100% until the RMSE for a validation data set converged to a minimum value. The initial parameter bounds were equal to the C3 and C4 plant parameters (Table S1). This approach was used to avoid parameter constraints that were either too restrictive or too unrestrictive. The RMSE between the modelled FCO2 and the measured FCO2 was used as the objective function for optimization. The study period was used as a training data set and the period of Jul–Aug 2014 as a validation data set. The parameter set with the lowest RMSE value according to the validation data set was used to model FCO2 for the complete study period.

average P sums resulted in low VWC throughout the vegetation period (April 2015–August 2015) with a mean VWC of 0.07 m3 m−3.

3.2. Green roof CO2 exchange 3.2.1. Potential influence of aircraft and diffuse CO2 emissions on FCO2 To analyze the potential influence of aircraft emissions on FCO2, we examined the dependence of FCO2 on the number of nocturnal flights (arrivals and departures) per 30 min (Fig. 2). This approach was chosen as the nocturnal footprint size is considerably larger than the daytime

3. Results 3.1. Meteorological characterization of the study period The study site is characterized by a temperate, warm and humid climate (Cfb after Koeppen and Geiger). Data from 1981 to 2010 serves as the climate normal period for comparison purposes with an annual mean TA of 9.3 °C (Table 1, Fig. 1). During the study period, temperatures were elevated by 1.6 °C compared to the climate normal period. The highest TA during the study period was reached on 07 August 2015 during a dry period with 37.6 °C (Fig. 1). The lowest TA occurred on 28 December 2014 with −8.4 °C. P was lower by 25% compared to the climate normal with large negative deviations in Feb 15 (−76% of climate normal monthly sum), May 15 (−73%) and Aug 15 (− 56%). Lower than

Fig. 2. (a) Nocturnal CO2 fluxes (FCO2) and (b) the variance of FCO2 in dependence on nocturnal flights per 30 min at the airport Berlin-Schönefeld.

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Fig. 3. Statistical properties of CO2 fluxes (FCO2) in dependence on wind direction and day of week, for situations in which the 90% footprint was larger than the roof fetch. N is the absolute number of data values for each box-whisker.

footprint due to the influence of more stable situations. A systematic increase of FCO2 or the variance of FCO2 with an increasing number of flights is not evident. Furthermore, for only 6 half-hourly FCO2 values (0.03% of data) the 90% footprint isoline was closer than 100 m to the northern runway (no data in case of the southern runway; Table S2). Two of these flux values were excluded because of bad quality flags according to Foken et al. (2004) and would have been excluded because of rain/dew otherwise. The remaining 4 flux values were b1 μmol s−1 m−2 and non-suspicious. The low number of data is explained by the large distance to the runways, by the fact that the runways were not within the main wind directions, and only one runway was operating at a time (c.f. Fig. S1). The statistical properties of FCO2 do not show any clear dependence on the day of week for any of the prevailing wind directions, which is especially evident for wind sectors with a high number of data (N; Fig. 3).

3.2.2. Gap-filling and annual cumulative NEE A total of 1358 data gaps resulting from the QA/QC procedure had to be filled. The majority of gap lengths are ≤1 h (69%; Fig. S3), and 98% of gaps are ≤24 h. The maximum gap length of 140.5 h was due to a rainy period between 26 December 2014 and 01 January 2015. In terms of our BE methodology (c.f. Section 2.2), MDS was the best gap-filling method for 62% of the data, followed by ANN (30%) and MDV (8%). Overall, the BE method (cf. Section 2.2) performed slightly better than using MDS (Table 2). The final annual green roof NEE resulted in an uptake of − 313 g CO2 m− 2, equivalent to − 85 g C m− 2 (Fig. 4, Table 2).

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Fig. 4. Cumulative NEE during the study period: (a) measured data with data gaps filled by the different methods, (b) simulated values by different gap-filling methods.

Table 3 Seasonal variation of CO2 fluxes (FCO2; μmol s−1 m−2) statistical values, air temperature (TA), substrate temperature (TS) and volumetric water content of the substrate (VWC) during the study period (SP). FCO2 mean is the arithmetic mean, FCO2 day the mean value between 10 and 16 CET, FCO2 night the mean value between 22 and 04 CET, FCO2 95th the 95th percentile and FCO2 5TH the 5th percentile for the respective period.

FCO2 mean FCO2 day FCO2 night FCO2 95th FCO2 5th TA 95th Ta 5th TS 95th TS 5th VWCmean

SP

SON

DJF

MAM

JJA

−0.22 −2.00 0.99 1.87 −4.03 24.82 −0.86 29.95 0.3 0.09

−0.03 −1.87 0.95 1.70 −3.54 20.37 2.98 21.27 2.97 0.10

0.32 −0.32 0.57 1.31 −1.03 9.04 −3.35 7.20 0.18 0.16

−0.74 −3.07 0.97 1.70 −5.06 19.32 1.74 24.90 2.38 0.08

−0.41 −2.70 1.45 2.67 −4.27 30.56 11.85 36.80 13.85 0.05

3.2.3. Seasonal variation of green roof CO2 exchange During the study period, FCO2 was slightly negative on average with −0.22 μmol s−1 m−2 (Table 3). Within late fall the photosynthetic activity of the plants ceased, which lead to a slightly positive flux throughout the following winter months (Fig. 5). Photosynthetic activity started to increase again in February resulting in slightly negative FCO2 around noon. Afterwards the daily time period during which negative FCO2

Table 2 Performance of marginal distribution sampling (MDS), artificial neural network (ANN) and mean diurnal variation (MDV) gap-filling methods and the best estimate (BE) and NEE for the study period. Performance measures and NEE

MDS

ANN

MDV

BE

RMSE (μmol s−1 m−2) R2 Slope Intercept NEE (g CO2 m−2 year−1) Model estimate Gap-filled FCO2 data NEE (g C m−2 year−1) Model estimate Gap-filled FCO2 data

0.88 0.84 1.00 −0.01

0.93 0.82 1.00 0.00

1.13 0.74 0.98 −0.02

0.83 0.84 1.01 −0.01

−310 −306

−320 −318

−398 −367

−301 −313

−85 −84

−87 −87

−109 −100

−82 −85

Fig. 5. Seasonal and daily variation of CO2 fluxes (FCO2) over the course of the study period.

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Fig. 6. Mean monthly diurnal courses of CO2 fluxes (FCO2; μmol s−1 m−2) during the study period. The transparent ranges indicate ±1 standard deviation. Monthly value of NEE of the best estimate (BE; g CO2 m−2) is specified.

prevailed increased continuously until May. March was the first month with higher photosynthetic activity than respiration and a monthly NEE of −9 g CO2 m−2 (Fig. 6). The highest CO2 uptake occurred during the growth period in MAM, with May showing the highest uptake rates (Fig. 6). In the following summer months mean FCO2 declined, which went along with reduced daytime CO2 uptake and enhanced nocturnal respiration (c.f. Table 3). Mean daytime uptake was lower in June and August compared to July (Fig. 6). This can be attributed to lower than average precipitation in these two months and above average precipitation in July. The seasonal temperature (air and substrate temperature) and respiration values indicate a similar variation (Table 3). Between October and March green roof respiration overcompensated daily CO2 uptake, which changed again around mid-March (Fig. 7). Daily uptake peaked on 07 May 2015 with −9 g CO2 m−2 day−1. This date marked a turning point in daily FCO2, i.e. daily uptake declined and reached positive values for a short period between the end of June and the first week of July. This coincided with shorter daytime periods during which FCO2

Fig. 7. Substrate moisture (VWC) as well as LAI (a) and daily CO2 fluxes (FCO2; b) during the study period. A Savitzky-Golay algorithm was used for smoothing FCO2 data in (b).

was negative (Fig. 5). The decrease in CO2 uptake was caused by very low VWC and declining LAI values. This was a result of degradation of green roof vegetation due to lower than average precipitation sums (Fig. 7). The vegetation recovered in July 2015 supported by higher

Fig. 8. (a) Mean daytime CO2 fluxes (FCO2; 10-16 CET), (b) mean night-time FCO2 (22-04 CET) and substrate moisture (VWC) during dry periods. Dry periods 1 (19–31 August 14), 9 (13–31 May 15), 10 (28 July–15 August 15) are highlighted by thick lines and symbols. The fit is given for the mean of dry periods 1, 9 and 10, with y = 0.23x − 5.05 and R2 = 0.78.

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water availability, for which reason daily FCO2 turned negative again until the end of the study period. 3.2.4. NEE during dry periods Dry periods were studied in order to evaluate the impact of declining soil moisture and the influence of meteorological variables on respiration and photosynthetic uptake. Considering all 10 dry periods which occurred during the measurement period, we found that CO2 uptake was approaching 0 μmol s−1 m−2 in dry periods 1, 9, 10 (Fig. 8 a). Dry periods 1 and 10 occurred during August 2014 and August 2015, respectively whereas dry period 9 occurred in late May 2015. The decline in CO2 uptake correlates with declining VWC starting from around 0.05 m3 m−3 (Fig. 8 c). Considering all dry periods FCO2 shows a clear dependence on K↓, which represents the seasonal gain and decline of biomass and photosynthetic activity (dry periods occurred during all seasons, Fig. 9). When VWC b 0.05 m3 m−3, the dependence of FCO2 on K↓ disappears and FCO2 becomes highly dependent on VWC (Fig. 10). The influence of the other meteorological variables seems of minor importance as documented by lower coefficients of determination. Whereas daytime FCO2 was declining linearly during the dry periods 1, 9 and 10 (Fig. 8 a), night-time respiration was apparently not influenced by declining VWC (Fig. 8 b). The general characteristic of the reduction in daytime productivity during dry periods is best captured by ANN (Table S3), having a similar fit compared to the one given in Fig. 8 a for measured FCO2. This supports

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our assumption that gap-filling methods show different performances during certain meteorological conditions despite their overall performance ranking (c.f. Section 2.2).

3.2.5. Physiological characteristics by means of modelling Using the published parameter values for C3 and C4 plants in the A-gs model resulted in overestimations of the measured flux (Table S4). Estimating the physiological parameters (cf. Section 2.4) by using the RMSE as objective function leads to a considerable improvement in the representation of the measured green roof CO2 surface-atmosphere exchange. The modelled FCO2 values follow the seasonal variation of the measured values reasonably well (Fig. 6). The new green roof parameter set (Table S1) gives insight into the (bulk) physiological characteristics of the green roof plant community, which primarily consists of Sedum species and complemented by herbaceous plants. The maximum quantum use efficiency is lower than the average values for C3 and C4 plants with 0.006 mg J−1 PAR compared to 0.014 and 0.017 mg J−1 PAR, respectively. According to the model results, the maximum stomatal conductance (gsmax) is 1.49 mm s−1, which corresponds to 63 mmol m−2 s−1. The average daily maximum (gsmax avg) during the vegetation period was 0.90 mm s− 1 or 38 mmol m−2 s−1, respectively. Expressed as minimum stomatal resistance (rmin), this translates to 671 s m−1.

Fig. 9. Relationship between meteorological variables and CO2 fluxes (FCO2) for dry periods 1–10. The seasons are color coded. Green = spring, red = summer, blue = fall and black = winter. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 10. Relationship between meteorological variables during dry periods 1, 9 and 10.

4. Discussion The extensive green roof was a carbon sink during the study period with a cumulative NEE of −85 g C m−2 year−1. Under the assumption that the cumulative carbon uptake scales linearly with σf, the annual carbon uptake would increase to −212.5 g C m−2 with full vegetation cover. This corresponds to reported green roof values of −187.5 g C m−2 year−1 for a site in Michigan, USA (Getter et al., 2009) and to mean values of several grassland sites throughout Europe (−150 ± 200 g C m−2 year−1; Gilmanov et al., 2007). The study period was drier than average, which resulted in a decreasing LAI in May and June (Heusinger and Weber, 2017). Hence, we expect that the cumulative annual NEE is lower in comparison to wetter years, during which daily NEE may not turn positive during summer, as was the case during June 2015. The potential influence of aircrafts as well as diffuse emission sources on FCO2 was studied. Our analysis indicates that aircraft arrivals/ departures had no influence on FCO2 and the variance of FCO2. Furthermore, we could not find evidence that the statistical properties of FCO2 on Sundays are different to weekday FCO2. Hence, we conclude that our measured FCO2 data is not significantly influenced by anthropogenic emission sources so that FCO2 data is comparable to other green roof sites. EC data gaps were filled by means of three well-established methods. Whereas all showed good performances concerning their goodness of fit, MDS and ANN were characterized by lower RMSE compared to MDV. This indicates that they are superior to more simple

approaches, i.e. MDV, and should be favored for gap-filling purposes (Moffat et al., 2007). In the present study we proposed a best estimate methodology, which is based on a RMSE with a moving time window. Our assumption was that each gap-filling methodology might have specific advantages during certain meteorological situations. It could be shown, that the ANN approach is useful during dry periods (c.f. Section 3.2.4). In contrast, MDS underperformed during dry periods, since VWC values are not used as input. We conclude that the combination of different approaches results in more robust NEE estimates. This is in agreement with an earlier study, which reported that application of several gap-filling methods increases the confidence in the source or sink strength of vegetated ecosystems (Soloway et al., 2017). During a dry period in May, VWC values were below the wilting point of herbaceous plants which lead to declining LAI values and subsequently positive NEE for a short period at the end of June. A closer look at summerly dry periods (1, 9 and 10) revealed that daytime carbon uptake declines linearly with VWC values ≤ 0.05 m3 m−3, whereas respiration appears rather unaffected. This is in agreement with others, who concluded that respiration is unrelated to soil moisture under mild drought conditions (Novick et al., 2004). Hence, VWC should be N0.05 m3 m−3 during the vegetation period to optimize CO2 uptake of the present green roof. To take a closer look into the physiological characteristics of the ‘bulk’ extensive green roof plant community, the A-gs model was applied and parameter values estimated by a genetic algorithm approach. The estimated green roof parameters seem to be plausible. The

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estimated quantum yield efficiency, e.g. is lower than values given for C3 and C4 plants, which would indicate that Sedum plants have relatively slow growth rates. Consistently, gsmax is lower with 1.49 mm s− 1 than reported average values between 6 and 12 mm s−1 for natural vegetation and crops (Kelliher et al., 1995). Nevertheless, gsmax avg with 0.90 mm s−1 (38 mmol m−2 s−1) is comparable to experimental data for green roof plants under dry conditions (VWC b 0.15 m3 m−3; Blanusa et al., 2013). The value of rmin (671 s m−1) is also similar to experimental values which were found to be in between 500 and 700 s m− 1 and typical values for succulent plants (Tabares-Velasco and Srebric, 2012). Under drought conditions daytime stomatal resistance values of over 1000 s m− 1 were reported before for typical green roof Sedum plants (Starry et al., 2014). Despite the fact that individual Sedum species demonstrate CAM characteristics (Earnshaw et al., 1985), a nocturnal carbon uptake was not observed for the studied green roof. Whether a nocturnal opening of the stomata lead to reduced nocturnal respiration would require experimental data on the stomata conductance, which was not available in this study and should be studied in future campaigns. An important point in addressing the benefit of urban green roofs in sequestering CO2 is related to the long term variation of green roof NEE. The green roof at BER was installed in 2012. Hence, our one-year measurement campaign took place on a newly established green roof. How the NEE extrapolates into the future is an open question, given the change in vegetation cover and the variation in annual meteorological conditions. At this point we are not able to quantify the long-term annual variation of the NEE estimate as measured by EC. The longterm variation of green roof NEE as well as the analysis of the fate of C after incorporation into plant biomass would be desirable for future studies. However, they were beyond the scope of the present research. 5. Summary and conclusions Eddy covariance measurements of the surface-atmosphere exchange of CO2 were performed over a full annual cycle on an extensive green roof in Berlin, Germany. It was demonstrated that the green roof was a carbon sink on an annual basis with an uptake rate of 85 g C m−2year−1. However, the photosynthetic CO2 uptake was dependent on substrate water availability. During dry conditions and low VWC, green roofs can turn into CO2 sources even during the vegetation period. Analysis of dry periods documented that VWC should be N0.05 m3 m−3 in order to optimize CO2 uptake of the present green roof. A set of parameters for the A-gs model was estimated to study physiological characteristics of the bulk green roof vegetation. The A-gs model is able to simulate the green roof CO2 surface-atmosphere exchange reasonably well on a daily basis. However, since the A-gs modelled FCO2 shows a small systematic error, we do not recommend the current parameter values for estimation of annual cumulative values. An optimized water availability will help to improve the carbon uptake of the vegetation and will be the focus of further studies. Acknowledgments We thank Jochen Heimberg (Environment Department, Berlin Brandenburg Airport) for permission to use the green roof site for EC measurements. We appreciate the help of Hagen Mittendorf (Climatology and Environmental Meteorology group, TU Braunschweig) during the field measurements. We also thank the anonymous reviewers for their constructive comments that helped to improve the quality of our earlier submission. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Appendix A. Gap-filling methods For applying the MDS method the REddyProc Tool (Reichstein et al., 2005) was used. The input variables were shortwave radiation (K ↓;

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W m−2), air temperature (TA; °C), soil temperature (TS; °C), vapor pressure deficit (VPD; hPa) and friction velocity (u*; m s−1). For designing the ANN the neural net time series toolbox of Matlab 2016a was used (Jain et al., 1996). A nonlinear autoregressive network with exogenous inputs (NARX) was chosen, which is specifically well suited to learn to predict a variable based on past values of the same variable and additional variables (exogenous inputs). The ANN consists of at least to layers, each consisting of at least one neuron. In each layer the input signal is weighted and summed for each neuron. A transfer function determines in which way the signal is transmitted to the next layer and can be thought of as an activation function. With each iteration the weights of the neurons are adjusted via supervised learning backpropagation. This means that the error between the output value and the measured value is used to adjust the weights until the error is below a predefined threshold. In this study in total three layers were used: an input layer, a hidden layer – without direct contact to neither input nor output - and an output layer. The network was trained by applying the Levenberg-Marquardt algorithm for optimization of the mean squared error. A sigmoid transfer function was used between hidden layer and output layer and a linear transfer function between the output layer and the output. The data was divided randomly so that 70% of the data was used for training, 15% for validation and another 15% for testing of the trained ANN, in order to minimize overfitting. Training data and validation data are both used for designing the ANN. When the training process is finished, a completely independent test data set is used to finally test the ability of the model to generalize reasonably well. Input values were scaled to the range of − 1 to 1. The seasonal variation was described by a sinus function ranging from 0 (middle of winter) to 1 (middle of summer). K ↓, TA, TS, volumetric water content of the substrate (VWC), VPD and u* were used as additional input data. The best combination for the number of delays and hidden neurons was found by minimizing the mean squared error between measured FCO2 values and the ANN output, which resulted in 8 delays and 19 hidden neurons in one hidden layer. For the MDV method, a mean diurnal average was calculated within a moving time window of ± 8 days around the missing value as suggested by Bamberger et al. (2014). The performance of all three methods was compared using the root mean squared error (RMSE), the linear regression coefficients of the slope and intercept and the adjusted coefficient of determination R2. Appendix B. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2017.07.052.

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