Evaluating closed chamber evapotranspiration estimates against eddy covariance measurements in an arctic wetland

Journal of Hydrology 578 (2019) 124030

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Research papers

Evaluating closed chamber evapotranspiration estimates against eddy covariance measurements in an arctic wetland Gillian Simpsona,

⁎,1

a b

T

, Benjamin R.K. Runkleb, Tim Eckhardta, Lars Kutzbacha

Institute of Soil Science, University of Hamburg, 20146 Hamburg, Germany Department of Biological and Agricultural Engineering, University of Arkansas, Fayetteville, AR 72701, USA

ARTICLE INFO

ABSTRACT

This manuscript was handled by Corrado Corradini, Editor-in-Chief Keywords: Evapotranspiration Polygonal tundra Upscaling Penman-Monteith Northern Siberia

Evapotranspiration (ET) is an important hydrological flux with a strong influence on greenhouse gas emissions from thawing permafrost. This study examines the suitability of the closed chamber method in characterising the spatial heterogeneity of ET fluxes in a typical polygonal tundra landscape in the Lena River Delta, northern Siberia. Actual evapotranspiration is compared across scales using: (1) ground-based chamber measurements to observe ET at the microsite scale; and (2) tower-based eddy covariance (EC) measurements which provide spatially averaged ET observations at the ecosystem level. Adopting an upscaling approach using EC estimates as a benchmark, the authors assess the suitability of the closed chamber method in an arctic wetland environment. A short closure time (40 s) and a linear model to describe the change in chamber headspace water vapour concentration were employed to estimate ET rates from the chamber measurements. Using a correction factor chamber fluxes were successfully scaled to the EC data. Yet, findings suggest that the performance of the closed chamber method is highly sensitive to the prevailing hydrometeorological conditions, and it is likely that the sorption and desorption of water molecules to the inside of the chamber and tubing has a strong impact on results. A number of methodological issues are presented in this paper which question the use of closed chamber measurements as a stand-alone tool for measuring ET in arctic wetland environments. However, when paired alongside a trusted benchmark, chambers can provide valuable information on ET at the microsite scale.

1. Introduction Evapotranspiration (ET) is a key water flux in arctic wetland environments. It inherently links the energy balance and water cycle; with latent heat flux constituting the main energy sink on seasonal and longer time scales (Kane et al., 1990; Wessel and Rouse, 1994) and a major part of the summertime landscape water balance (Helbig et al., 2013). Due to the tight coupling between energy, water, and biogeochemical systems in arctic wetlands, ET is an important control on greenhouse gas emissions in permafrost areas, as the water-table determines the relative proportion of soil carbon released to the atmosphere as CO2 or CH4 (Christensen et al., 2004; Schädel et al., 2016; Strack et al., 2004). Despite the importance of ET, there is poor understanding of this flux and its response to climate change in arctic areas (Bring et al., 2016; Hinzman et al., 2013; Vihma et al., 2016). In arctic wetlands, ET is particularly complex due to the influence of

patterned ground such as the polygonal tundra, which covers much of the Alaskan and Siberian lowlands (e.g. Black, 1982; Romanovskii, 1985). Ice wedges under the soil surface create a pronounced microrelief with variations in vegetation, hydrology, soils, and biogeochemical processes on the scale of a few meters (Brown et al., 1980; Helbig et al., 2013; Kutzbach et al., 2004; Sachs et al., 2010; Steedman et al., 2017; Vourlitis et al., 1999). To date, few studies have examined how micro-topography affects ET in heterogeneous tundra landscapes (Muster et al., 2012; Raz-Yaseef et al., 2017; Young-Robertson et al., 2018). This gap in the literature limits our ability to understand the fine-scale controls on this spatially variable flux and to predict how ET in arctic ecosystems will change in the future both directly as a result of climate change (McVicar et al., 2012; Overland et al., 2017; Willett et al., 2008) and indirectly via permafrost degradation or geomorphological change (Liljedahl et al., 2016). In regions with extensive polygonal tundra such as the Lena River Delta in north-eastern Siberia, it

Abbreviations: ALT, Active layer thickness; Tair, Air temperature; EC, Eddy covariance; IRGA, Infrared greenhouse gas analyser; Rn, Net radiation; P-M, PenmanMonteith; PAR, Photosynthetically active radiation; RH, Relative humidity; VPD, Vapour pressure deficit; WRB, World reference base ⁎ Corresponding author. E-mail address: [email protected] (G. Simpson). 1 Current address: School of Geosciences, Crew Building, University of Edinburgh, Alexander Crum Brown Road, Edinburgh EH9 3FF, Scotland, UK. https://doi.org/10.1016/j.jhydrol.2019.124030 Received 1 May 2019; Received in revised form 3 July 2019; Accepted 7 August 2019 Available online 09 August 2019 0022-1694/ © 2019 Elsevier B.V. All rights reserved.

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Fig. 1. (a) The distribution of permafrost and location of the Lena River Delta in the circumpolar region (Hugo Ahlenius, UNEP/GRID-Arendal, 2007, http://gridarendal.herokuapp.com/resources/5234). (b) The Lena River Delta, north-eastern Siberia (Landsat-7 Enhanced Thematic Mapper plus image, GeoCover 2000, NASA), and location of Samoylov Island marked by the white square.

has been suggested that regional- and global-scale models which neglect small-scale polygonal features could produce strongly biased estimates of ET (Muster et al., 2012; Virtanen and Ek, 2014). In terms of monitoring, eddy covariance (EC) is a commonly adopted technique for measuring ET in permafrost environments (e.g. Langer et al., 2011; Lund et al., 2017; Vourlitis et al., 1999), with regional networks facilitating comparisons of water vapour fluxes across sites (Baldocchi et al., 2001). However, this method provides only spatially averaged flux estimates at the whole-ecosystem scale. The impact of a heterogeneous land cover on fluxes can only be quantified with observations at the microsite scale e.g. using weighing lysimeters (Muster et al. 2012), water balance calculations (Helbig et al. 2013), and the closed chamber method (Cohen et al., 2015; Raz-Yaseef et al., 2017; Young-Robertson et al., 2018). The latter is particularly appropriate for measuring exchange between low-standing vegetation and the atmosphere (Vourlitis, Oechel, Hastings, & Jenkins, 1993). Furthermore, when instrumented with infrared greenhouse gas analysers (IRGAs), chamber systems are suited to conduct rapid spatial survey measurements (< 5 min) not possible with other field-techniques. Although chamber-based ET estimates are vulnerable to a range of errors (see Livingston and Hutchinson, 1995), studies conducted in warm, temperate climates find that this technique can produce accurate measurements of water vapour exchange (McLeod et al., 2004; PérezPriego et al., 2010; Reicosky et al., 1983). In cold arctic environments however, and despite the popularity of chambers for studying smallscale variability in CO2 and CH4 fluxes (e.g. Pirk et al., 2016; Sachs et al., 2010; Vourlitis et al., 1993), little attention has been given to evaluating their suitability for measuring ET. To our knowledge, the only studies where chamber measurements of this flux have been validated in an arctic tundra ecosystem were conducted on a coastal plane at Barrow, Alaska (Cohen et al., 2015; Raz-Yaseef et al., 2017; YoungRobertson et al., 2018). These studies are based on a one-week field pilot study in 2013 (Cohen et al., 2015), followed by three months of automated chamber data (June-September 2013), and two one-week portable chamber campaigns (July 2013, 2014) (Raz-Yaseef et al., 2017; Young-Robertson et al., 2018). At Barrow, chamber measurements of ET have been used to capture diurnal cycles, and different ET rates across plant functional types and geomorphological units. Recently, the potential of chambers to aid partitioning of ET into its evaporation and transpiration fluxes has highlighted temporal and spatial variability in these components (Young-Robertson et al., 2018).

Although the chamber setups used in Barrow were calibrated against a laboratory-based balance approach, a large gap of over 40% has been identified between chamber and EC flux rates (Raz-Yaseef et al., 2017). This disparity has been attributed to their different footprint in the landscape, although a more robust analysis is pending. We build on the work of the above authors to further test the chamber method at a different site; the polygonal tundra in the Lena River Delta. Employing a nested approach, we assess the performance of the chamber method in measuring actual ET by comparing: (1) ground-based chamber measurements providing ET estimates at the microsite scale (0.1–10 m2); with (2) tower-based EC measurements which produce spatially averaged ET estimates at the ecosystem scale (100–10,000 m2). To our knowledge this is the first study to adopt an upscaling approach to robustly test chamber ET estimates using EC as a benchmark. Using two years of full growing season data from a new site, we aim to: (1) characterise the spatial and temporal heterogeneity in smallscale ET captured by the closed chamber method; and (2) assess the performance of the closed chamber method for measuring ET rates in the Siberian Arctic. 2. Study site This work was conducted on Samoylov Island (72°22′N; 126°30′E), one of the 1500 islands in the extensive Lena River Delta in northeastern Siberia (see Fig. 1). The island lies within the zone of continuous permafrost, which extends to depths of 500–600 m in the study area (Zhang et al., 1999), and has been the location of much research including studies of the carbon cycle and water balance (see Boike et al., 2019, 2013). Climate at the site is arctic continental, with a low annual rainfall of 125 mm, and a mean annual air temperature of −12.5 °C (1998–2011) (Boike et al., 2013). Temperature follows a strong seasonal cycle, with a range in monthly average temperatures of over 40 °C (Boike et al., 2013). Snowmelt commences around the middle of May, and the growing season extends from mid-June to midSeptember, with the highest temperatures in July and August. A more detailed description of baseline characteristics for Samoylov Island is provided by Boike et al. (2013). The island covers an area of about 5 km2 and is comprised of two geomorphological units. The western part consists of an active floodplain (∼2 km2); and the eastern part (∼3 km2), where this study was 2

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Fig. 2. Map of the study site on Samoylov Island, north-eastern Siberia: (a) high resolution land cover map of Samoylov Island (0.14 m, Boike et al., 2012); (b) aerial image showing polygonal tundra on the river terrace with locations of the eddy covariance tower (EC) and reference polygon where closed-chamber ET measurements were conducted indicated by the white rectangles (Boike et al., 2015). The distance from the EC tower to the reference polygon is 30 m; (c) the reference low-centred polygon, which is around 10 m in length. Eight permanent chamber measurement plots were established: four in the polygon centre (shown in blue) and four on the polygon rim (shown in orange). The IRGA was kept stationary, connected to the chamber via 10 m long tubing. A boardwalk provided access to chamber measurement plots.

3.1. Spatial and temporal variability of ET

conducted, is formed of sediments from a Holocene river terrace. The terrace landscape is a wetland complex characterised by ponds, thermokarst lakes, and polygonal lowland tundra (Boike et al., 2013). Polygons are around 5–20 m wide and typically low-centred, with an elevation difference of 0.2–1.0 m between the wet centres and raised rims (Muster et al., 2012). Ground-based chamber measurements were conducted across a lowcentred reference polygon in the centre of the river terrace (see Fig. 2). This polygon was selected due to: (i) features representative of lowcentred polygons in the region; and (ii) close proximity to the EC tower (30 m, Fig. 2b). Fig. 2c shows the two microsites examined: the wet polygon centre and the drier polygon rim. The maximum active layer thickness (ALT) at the polygon centre was around 40 cm and was deeper than measured at the polygon rim (30 cm). The reference polygon centre was depressed and poorly drained, with soils classified as Reductaquic Cryosols (Hyperhumic) according to the WRB system (IUSS Working Group WRB, 2015). Cryoturbation was evident at the polygon rim with its Turbic Glacic Cryosols and a water table positioned only a few centimetres above the permafrost table. A vegetation survey conducted prior to sampling found that the wet polygon centre was predominantly covered by mosses (e.g. Drepanocladus revolvens and Meesia triqueta), hydrophilic sedges (e.g. Carex aquatilis, Carex chordorrhiza), and marsh cinquefoil (Comarum palustre). In contrast, the polygon rim was dominated by mosses (e.g. Hylocomium splendens, Polytrichum sp., Rhytidium rugosum), with some lichens (e.g. Peltigera aphtosa, Stereocaulum sp, Cladonia rangiferia), a number of dwarf shrubs (e.g. Dryas punctata, Salix glauca, Salix reticulata), and other vascular plants (e.g. Pyrola rotundifolia, Astragalus frigidus and Saussurea sp.).

3.1.1. Closed-chamber ET fluxes 3.1.1.1. Experimental setup. ET was measured almost daily at the reference polygon using the closed-chamber method from 22 July–20 August 2014 during the first campaign, and from 11 July–22 September in 2015. Details of the chamber setting and operation are provided by Eckhardt et al. (2019), and are therefore described only briefly here. Eight permanent measurement plots were established (four per microsite), and a PVC collar was inserted into the active layer at each plot several days before sampling commenced. An airtight seal between the collar and the chamber was achieved using a silicone rubber Ushaped frame filled with water. Chamber experiments were performed manually using a 0.5 m × 0.5 m × 0.5 m clear chamber and lid (both transparent to > 90% PAR). To minimise the effect of pressure shocks upon chamber setting (Hutchinson and Livingston, 2001; Livingston et al., 2006), two vents in the upper chamber surface were closed shortly after deployment. A small fan inside the chamber ensured that headspace air was well-mixed, with average fan-induced wind speed estimated as 0.37 ± 0.12 m s−1 using a sonic anemometer (Solent R3, Gill Instruments, UK). Chamber headspace trace gas concentrations (H2O, CO2 and CH4) were measured for 2 min using an IRGA at 1-second intervals (UGGA, Los Gatos Research Inc., Canada). This analyser was connected to the chamber via plastic tubing (length 10 m), which was opaque (black) during the first campaign, and replaced with clear polyurethane tubing for the second campaign to allow visible detection of condensation. Environmental conditions inside the chamber were recorded using a PAR sensor (SKP 215, Skye Instruments Ltd., UK) and temperature probe (107 Thermistor probe, Campbell Scientific Ltd., USA) by a CR800 data logger (Campbell Scientific, Germany). In addition, daily measurements of thaw depth, soil temperature and moisture at 5 cm depth (GS3, Decagon Devices, Inc., USA) were made next to each plot. Water table depth was monitored manually at both microsites in 2014, and by use of a water level logger (Diver, Schlumberger, USA) installed at the polygon centre in 2015. Meteorological data from the nearby EC tower were employed to provide additional variables for analysis.

3. Methods This study analyses ET flux measurements made on Samoylov Island over two growing seasons: July-August 2014; and the extended period July-September 2015. We employed data from numerous sources including our ground- and tower-based measurements of ET, meteorological data from the EC tower, and manually collected environmental variables from the reference polygon. A schematic overview of our methodology to upscale the ground-based chamber measurements is provided in Fig. 3.

3.1.1.2. Flux calculation. Water vapour fluxes inside the chamber were 3

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Fig. 3. Schematic overview of the methodology employed in this study. The highly heterogeneous polar landscape was first divided into wet- and dry tundra, overgrown- and open water classes. ET rates for the former two land cover types were estimated by adjusting the Penman-Monteith model for reference evapotranspiration to the chamber data; and the latter two were combined into one class, “water”, for which ET rate was derived using a flux gradient model. EC footprint data and a high resolution land cover map were then combined to estimate the areal coverage of each land cover type per 30-minute EC footprint period. Respective ET rates for each land cover type were multiplied by their weighted areal coverage inside the footprint to obtain a chamber-based estimate of ET for the entire footprint area. These ground-based estimates were later compared against the tower-based EC estimates.

calculated as per Kutzbach et al. (2007) using a MATLAB® routine (Eckhardt and Kutzbach, 2016). We estimated the transit period of gas from the chamber to the IRGA using the pump speed and visual inspection to be around 20 s, the point after which we refer to as t = 0. The increase in water vapour concentration inside the headspace (dCH2O/dt) was plotted over the duration of each measurement. We examined the residuals of both linear and non-linear regression models to describe dCH2O/dt (see Table S1, Supplementary Material). In general, over a short period of 40 s, we found no clear visual difference in residuals from the two regression models. The extent to which a linear or non-linear fit was favoured statistically depended strongly on the time period examined, with notable differences between the 2014 and 2015 campaigns. Overall however, the differences between the non-linear and linear model were relatively small (see Supplementary Material). The linear model was favoured by the Akaike Information Criterion for small sample sizes (AICc, Burnham and Anderson, 2004), and the two regression models had similar adjusted coefficient of determination (R2adj) values. Furthermore, a visual analysis of the dCH2O/dt curves found that the non-linear fit sometimes gave false high flux rates at times of very low water vapour flux. Due to it being more robust, we decided that a linear model over a short period of 40 s was the most suitable for describing the change of water vapour concentrations in the chamber headspace, especially under the low-flux conditions experienced in 2015. This choice of a linear model, and the spurious high flux rates associated with a non-linear fit are in agreement with the Barrow chamber ET studies (Cohen et al., 2015; Raz-Yaseef et al., 2017; Young-Robertson et al., 2018). Finally, a variable amount of noise in the signal from the UGGA was visible in our results, and we used a two-tailed Student’s t-test to identify and remove flux estimates where the linear regression slope (dCH2O/dt) was not significantly different from zero (p < 0.1, n-2 degrees of freedom). This significance filtering successfully removed the majority of remaining spurious high flux rates from our analysis resulting from poor fitting of our linear model to the chamber data. It is well known that sorption of water vapour from the chamber to the connecting tube walls can cause a systematic underestimation of fluxes (Philip, 1963). We therefore performed an additional experiment in the field to compare calculated ET fluxes from a 1-m vs. a 10-m tube

arrangement, enabling us to reduce the impact of sorption on our results. Flux measurements were conducted in July 2018, between 09:00 and 12:00 local time at one of our permanent measurement collars on the polygon rim. We utilised the same setup and measurement procedure as in the 2014 and 2015 measurement campaigns with 2 min chamber closure periods. After each measurement, the chamber was flushed with ambient air, and the measurement was then repeated with a different length of tubing. Successive measurements were conducted using the 10-m and 1-m tubes, totalling six repetitions for each tube length. The cool and wet climatic conditions of these experiments were similar to those which prevailed during the 2015 campaign. Average air temperature recorded during our 2018 measurements was around 4 °C, net radiation was 139 Wm−2, RH was around 84%, and wind speeds were low at 4 ms−1 (Boike et al., 2019). Chamber ET fluxes calculated using a linear regression over a 40-second interval with a 1-m tube were consistently twice those using a 10-m tube (n = 12, mean scaling factor ± SD = 2.06 ± 0.29). The impact of tube-length on chamber ET fluxes therefore appears to be strong, consistent in magnitude, and should not be ignored. We decided to simulate the 1-m tube experiment on our data, minimising the impact of sorption on our results by correcting each original 10-m tube flux estimate by a factor of 2. This tubelength correction has been applied to all chamber flux estimates reported from hereon, and hence forms the basis of all subsequent modelling analyses (P-M modelling and upscaling). While the 2018 measurements did not span the full range of climatic conditions experienced over the 2014 and 2015 campaigns, we believe our scaling factor of 2 is likely to be the minimum scaling factor required to simulate a 1-m tube. During conditions conducive to high ET rates such as those observed in 2014, we expect this scaling factor would increase due to a greater amount of water vapour sorption to the chamber walls and tubing, in turn causing a larger underestimation of the true flux. Individual chamber ET (ETchamber) estimates during each measurement round (consecutive measurement of collars 1–8) were averaged separately for polygon rim and centre microsites. The time required to complete one measurement round was approximately 40 min, and so is comparable to the 30-minute EC flux intervals. Measurements with non-significant linear regression slopes were removed and a total of 108 measurement rounds for 2014 (52 centre, 56 rim); and 114 measurement rounds for 2015 (64 centre, 50 rim) were used for analysis. 4

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3.1.2. Eddy covariance ET fluxes 3.1.2.1. Setup and data processing. Data from an EC system stationed in the central part of the river terrace close to the reference polygon (see Fig. 2b) were used to provide a benchmark for our ETchamber estimates. The set-up involved a three-dimensional sonic anemometer (CSAT3, Campbell Scientific, Inc., USA) and an open-path infrared gas analyser (LI-7500A, LI-COR Biosciences, Inc., USA) located at a height of 4.15 m, with a horizontal displacement between sensors of approximately 20 cm. Raw data outputs were recorded at a frequency of 20 Hz (LI7550, LI-COR Biosciences, Inc., USA), and additional instruments measured meteorological variables. These variables included atmospheric pressure (CS100, Campbell Scientific, Inc., USA), air temperature (Tair) and relative humidity (RH; HMP45, Campbell Scientific, Inc., USA), incoming and outgoing solar radiation (NR01, Hukseflux Thermal Sensors B.V., Delft, the Netherlands). Flux analysis was conducted using the EddyPro® software (v6.0.0, LI-COR Biosciences, Inc., USA). Pre-conditioning of the data involved coordinate rotation and time lag removal (Aubinet et al., 1999; McMillen, 1988), after which LE fluxes were calculated across 30minute averaging intervals (Baldocchi et al., 2001). The WPL term was applied to correct for the effects of fluctuations in air density (Webb et al., 1980), and we adopted the meteorological convention whereby positive values indicate a net upwards flux of energy or matter to the atmosphere. Micro-meteorological quality control analysis was conducted to determine periods when the conditions necessary for EC were invalid, typically during times of very stable atmospheric conditions and low turbulence (Foken and Wichura, 1996). Subsequent gaps in the data were filled using the online “Eddy covariance gap-filling and fluxpartitioning tool” (Reichstein et al., 2005). This study examines only gap-filled LE data with a quality flag of 1 which is the most reliable (after the original data) as part of a 0–3 scale. For the 2014 campaign, gap-filling was performed for 17% of half-hourly flux estimates; all of which had a quality flag of 1. In contrast, for 2015 gap-filling was necessary for 30.8% of LE fluxes, largely due to a power failure event during which no data were collected (20–25 August 2015). This period of missing data was removed from our analysis, and all remaining LE fluxes were converted to ET rates in mm d−1.

measurement campaigns to 93.3 ± 3.6, meaning that on average over 90% of the footprint was underlain by the map. The remaining unclassified area, when the footprint exceeded the mapped area, was filled proportionately by each mapped land cover type for each footprint interval. Wind direction had a clear impact on the areal coverage of land cover types inside the footprint (Fig. 4). Overall, dry tundra areas were the most prevalent, whereas the other land cover types occupied a much smaller proportion of the mapped area (Table 1). A visual comparison of Fig. 4a and 4b shows that EC footprint periods with coinciding chamber measurements covered a similar distribution of winds and land cover types to the full campaign period. 3.2. Performance of the chamber method using EC as a benchmark 3.2.1. The Penman-Monteith model Calculated chamber fluxes were modelled using the PenmanMonteith (P-M) model (Monteith, 1965; Penman, 1948). ET in this physically based model is determined mainly by the available energy and vapour pressure deficit, but also includes aerodynamic and surface resistance terms which limit the flux:

QETchamber = ET =

s (Rn

G) + s+

(

a Cp VPD

ra

(1 + )

)

rs ra

(1)

where λ is the latent heat of vaporisation [J kg−1], s is the slope of the saturation vapour pressure versus the temperature curve [kPa K−1], Rn is the net radiation [Wm−2], G is the ground heat flux [Wm−2], ρa is the density of moist air at constant pressure [kg m−3], Cp is the specific heat capacity of air [J kg−1 K−1], VPD is the vapour pressure deficit [kPa], ra is the aerodynamic resistance [s m−1] (as per Pereira et al., 1999), ϒ is the psychrometric constant [kPa K−1], and rs is the bulk stomatal or canopy resistance [s m−1]. Due to the contrasting nature of the polygon rim and -centre microsites examined in this study, we employ rs as a fit parameter, which was first tuned against the measured chamber ET flux (separately for polygon rim and -centre sites) and then modelled using a multivariate linear regression based on environmental and meteorological variables (described in Section 4.2.1). The ground heat flux at the soil surface, G [Wm−2], was calculated by summing the heat flux recorded below the surface and the heat content change of fractions of water and dry peat in the soil, as performed by Wu et al. (2010):

3.1.2.2. Footprint analysis and mapping. We determined the source area of each 30-minute EC flux interval using the analytical footprint model of Kormann & Meixner (2001) for non-neutral stratification, employing a constant roughness length (z 0 = 0.13 m ). Analysis revealed that the majority (90%) of the measured flux was sourced within a 1000 m distance of the EC tower, with the peak flux contribution located 60 m of the tower. The most common wind directions spanned the sector 90°–180°, with south-easterly winds observed 44% of the time over both campaigns. During periods with stronger winds from the west and south-west however, the footprint area extended to cover a larger area of overgrown- and open water, including a number of lakes. Although the surface could not be considered fully homogeneous during these times, the relative error induced by including these areas is minimal during summer (Holl et al., 2019). To estimate the relative areal coverage of each land cover class inside the footprint per 30-minute source area, data from the footprint model were overlain with a high-resolution land cover map (Boike et al., 2012, see Fig. 2a), weighted by the footprint probability density function (Forbrich et al., 2011). Land cover in the map is heterogeneous and separated into four classes: (i) dry tundra; (ii) wet tundra; (iii) overgrown water; and (iv) open water, as described in Table 1. Unclassified parts (2.1% of the map) were assigned as a proportionate mix of the other four classes to enable full classification within the mapped extent. We termed the percentage of the footprint covered by the map image “sum image” (see Fig. 4), and to limit uncertainty, we removed instances where the sum image was < 80% from our analysis. This practice increased the mean sum image of our footprint for the two

G = G0 + CS

dTs . z. dt

S

+ CH

dTs . z. dt

H

(2)

−2

where G0 is the ground heat flux [Wm ] measured by heat flux plates (HFP01, Hukseflux Thermal Sensors B.V., Delft, the Netherlands) installed at a nearby polygon rim and -centre location, CS is the specific heat capacity of dry peat (0.6 J g−1 K−1), CH is the volumetric heat capacity of water (4.2 J g−1 K−1), Ts is the soil temperature [K], ρH (1.0 g m−3) and ρS (0.57 g m−3) refer to the density of water and dry peat respectively, and Δz is the thickness of the soil layer (0.1 m) above the heat flux measurement plates. Finally, our modelled chamber fluxes, QETchamber [Wm−2], were converted into units of mm d−1. 3.2.2. Upscaling ET Microsite-level chamber flux estimates were upscaled to make them comparable with the ecosystem-scale EC measurements; combining the above footprint analysis with flux rate estimates for each land cover type. We used polygon centre- and rim- adjusted versions of the P-M equation to approximate ET rates for wet and dry tundra areas. For areas of open- and overgrown water, as no direct ET measurements were available, these were combined into a single class (water), and ET flux rates [Wm−2] were modelled using a flux-gradient approach: 5

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Table 1 Areal coverage and description of the land cover classes: open- and overgrown water, dry-, and wet tundra areas inside our EC source area. Values shown denote the corrected* mean coverage inside the footprint weighted by the footprint probability density function averaged over the 2014 and 2015 campaigns, and correspond to footprint intervals where parallel chamber measurements were made, as shown in Fig. 4b. *Corrections made: (i) classified area inside map has been increased to 100% by applying a correction factor; (ii) for EC intervals where ≥80% of the footprint is mapped, the remaining unmapped area has been filled using the average areal coverage of all land cover types inside the map so that 100% of the footprint is classified, as explained in Section 3.1.2.2; (iii) EC footprint intervals where < 80% of the footprint is mapped have been removed from analysis. Land cover class

Mean coverage ± SD (%)

Description

Open water Overgrown water Dry tundra Wet tundra

4.9 ± 3.4 7.3 ± 1.9 70.0 ± 5.2 17.7 ± 3.3

Polygon centres, thermokarst lakes, no immersed vegetation Riparian areas, polygon centres, frost cracks, ≤15% areal plant cover Elevated polygon rims or centres, vegetation dominated by mosses Depressed centres, trenches, collapsed ridges, vegetation dominated by mosses

FH2 O =

air

ra

(q (zm )

q (z surf ))

baseline average, whereas the 2015 campaign was characterised by wetter conditions with precipitation totals that were 18% above average (per Boike et al., 2013). This rain fell across a number of events, including one heavy precipitation event where 17.9 mm rain fell over a 12 h period on 23–24 August 2015. These events caused considerable fluctuations in water table depth, with the reference polygon often remaining water-saturated up to the soil surface for a number of days. This situation is in strong contrast to 2014, where water table depth at the reference polygon was continuously below the surface and decreased steadily over the course of the summer. In addition to being wetter, the second campaign saw the growing season commence later than usual. Air temperatures in early summer (May and June 2015) were around 1 °C cooler than during the previous year, resulting in the delayed thawing of snow and ice, and subsequent later onset of vegetation growth. Despite this early disparity, monthly mean temperatures for August and September over the two years were similar and both close to the 1998–2011 average reported by Boike et al. (2013). Net radiation (Rn) was highly variable over the measurement campaigns due to changing synoptic weather conditions as well as the strong seasonal cycle. On average, daily Rn in 2014 was around 35% higher than in the 2015 campaign, with the largest difference between the two campaigns seen in July. Finally, the air was drier in summer 2014, where daily mean VPD was considerably higher (∼40%) than observed in 2015.

(3)

where air is the density of air [kg m−3], q is specific humidity [kg kg−1] measured from the height [m] of our EC tower (zm) and at the surface (zsurf), which was estimated from surface temperature measurements made at a shallow pond on Samoylov Island (Julia Boike, SPARC Group, Alfred Wegener Institute, personal comm., 2016) via the Magnus Formula (Alduchov and Eskridge, 1996). For aerodynamic resistance (ra) we employed a constant roughness length for calm water surfaces, z0, of 10−4 m (Garratt, 1994) in the same ra scheme as previous (Pereira et al., 1999). Finally, upscaled ET estimates for the EC source area per 30-minute flux interval (ETupscaled) were calculated by summing the relative areal coverage of each land cover type weighted by the footprint probability density function (Ai, %) with its respective modelled ET rate (ETi, mm d−1):

ETupscaled = (Adry ETdry )+(Awet ETwet ) + (Awater ETwater )

(4)

4. Results 4.1. Spatial and temporal variability of ET 4.1.1. Hydrometeorological conditions The two measurement campaigns varied considerably in their hydrometeorological conditions as shown in Fig. 5. The first campaign (2014) was during a relatively dry year with near-average air temperatures. Summer precipitation (May-September) fell 19% below the

4.1.2. Chamber ET fluxes Our calculated chamber fluxes display considerable variability both spatially between the two microsites, as well as temporally over the two

Fig. 4. Fractional coverage of each land cover type within the EC source area (weighted by the footprint probability density function) with reference to wind direction, and combined for the 2014 and 2015 measurement campaigns: (a) shows uncorrected data (i.e. includes sum image < 0.8, classified area < 100%) for the entire campaign period (22 July–20 August 2014, 11 July–22 September 2015; n = 4,597); (b) shows corrected data (i.e. sum image ≥0.8, classified area = 100%) from footprint intervals with corresponding chamber measurements only (n = 216). 6

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Fig. 5. Overview of the summer meteorological conditions spanning the two campaign periods. These are provided as daily means for ET measured at the eddy covariance tower (ETeddy), air temperature (Tair), net radiation (Rn), relative humidity (RH), and precipitation (PR). Values for 2014 and 2015 are shown in black and grey respectively. Missing EC data for 20–25 August 2015 in the uppermost plot are due to a gap in the EC dataset. In panel (e) we show daily precipitation for the two campaign periods as stacked bars, and cumulative daily precipitation over the study period as lines.

Fig. 6. Temporal and spatial variability of ET fluxes measured using the closed chamber method at the reference polygon over: (a) the 2014 campaign (ETcentre = 14.2 ± 8.3 mg m−2 s−1, ETrim = 10.6 ± 8.2 mg m−2 s−1); and (b) the 2015 campaign (ETcentre = 2.8 ± 1.9 mg m−2 s−1, ETrim = 2.2 ± 2.2 m−2 s−1). Shown are spatially averaged flux rates for the polygon centre (blue) and –rim (orange) microsites, and a time series of daily average ET for the two campaign periods which combines measurements from polygon rim and -centre rounds. Boxes have lines placed at the median, lower and upper quartiles. Whiskers extend to adjacent values in the data up to 1.5 times the interquartile range from the ends of the box. Outliers (values exceeding this range) are marked with a ‘+’.

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Fig. 7. ETeddy and ETupscaled estimates over the 2014 campaign period: (a) shows eddy covariance ET estimates (grey dashed line) against ET estimates from our chamber-based upscaling model (black solid line), with ET estimates from the chamber measurements (circles) provided for comparison; (b) decomposition of the ETupscaled flux into its three land-cover components: wet tundra, dry tundra, water. Note that fluxes for the three land-cover types were multiplied by their respective areal coverage inside the EC footprint (weighted by the footprint probability density function) and summed to produce an ETupscaled estimate for each half-hourly EC averaging period (see Eq. (4)).

campaigns (see Fig. 6). Average ETchamber from the 2014 campaign, calculated from all individual measurements at the reference polygon was 12.4 ± 8.4 mg m−2 s−1 (mean ± SD). This rate exceeds estimates for 2015, where average ETchamber was around 80% lower (2.5 ± 2.0 mg m−2 s−1), and there were few instances where ET rates were as large as the 2014 average. No strong seasonal cycle was visible in the chamber ET fluxes from the reference polygon (see Fig. 6). During the first campaign, near-daily flux measurements showed an increase in ET rates until the second week of August, where a maximum daily ET rate of 34 mg m−2 s−1 was measured. It follows that mean daily ET in August was over twice that recorded in July. In contrast, during the 2015 campaign flux rates were generally low throughout July and August, increasing to a maximum in late August – start of September. For 2015, mean monthly daytime ET was highest during September, and considerably lower during August and July. Examination of the two microsites also revealed some degree of spatial heterogeneity. On average, ET fluxes measured at the polygon centre exceeded those from the polygon rim (see Fig. 6). During the 2014 campaign for example, average ET at the polygon centre was over 30% larger than measured at the rim microsite. However, there were also cases where the opposite was true, and we observed considerable overlap in the range of flux estimates from the two microsites. The contrasting hydrometeorological conditions of the two campaign periods appear to have influenced the performance of the chamber technique. For the majority of cases in the 2014 campaign, we observed an initial increase in water vapour concentration (CH2O) upon chamber setting, as is characteristic of a closed system. In contrast, despite employing the same measurement procedure, data from the 2015 campaign reveal much shallower dCH2O/dt initial slopes. During this campaign, a constant or decreasing CH2O was the most common trend over the period of chamber deployment, and a high proportion of dCH2O/dt regression slopes were not statistically significant (> 45% in 2015 vs. 8% in 2014; both p < 0.1).

4.1.3. Eddy covariance ET fluxes As seen in the hydrometeorological and chamber data, contrasting flux rates were also measured by the EC system (Fig. 5a). In 2014, average daily ETeddy was 1.39 ± 0.65 mm d−1 for the period May–September, whereas in 2015 average daily ETeddy for the same period was 0.98 ± 0.63 mm d−1; equating to a reduction of around 20% on the previous year. Furthermore, a visual examination of Fig. 5 shows that the peaks of up to 3 mm d−1 observed in ETeddy (often coinciding with peaks in Tair and Rn) were less frequent and less pronounced over the 2015 campaign than in 2014. 4.2. Performance of the chamber method using EC as a benchmark 4.2.1. The Penman-Monteith model Our fitted P-M model for the 2014 campaign displayed good skill in predicting ETchamber (R2 = 0.67, RMSE = 4.86 mg m−2 s−1 for the polygon centre; and R2 = 0.67, RMSE = 5.28 mg m−2 s−1 for the polygon rim; both p < 0.01). Although employing surface resistance (rs) as a fit parameter resulted in unrealistic rs values (median rs: 331 s m−1), the model was able to maintain the characteristics of the chamber-measured fluxes, with average predicted ET rates at the polygon centre for the 2014 campaign (13.9 ± 8.4 mg m−2 s−1) exceeding those at the polygon rim (10.1 ± 9.1 mg m−2 s−1). In contrast, when the two campaign periods were considered jointly, we found it was not possible to fit the P-M model to the chamber fluxes at an acceptable significance level. Furthermore, solving Eqn. (2) for the surface resistance produced implausibly high values for this parameter (median rs: 763 s m−1 for 2014; 2037 s m−1 for 2015; both in the joint model), over twice those seen when employing only the 2014 data. In this joint model, calculated rs values based on the chamber data were over a factor of ten greater than those calculated using the EC data (median: 63 s m−1 for 2014; 58 s m−1 for 2015), and displayed much more variability over the two campaigns. The following analysis therefore focusses on data from the 2014 campaign only, for which we were able to model rs based on net radiation and relative humidity 8

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using a stepwise multilinear regression at an acceptable significance level (see Eq. (5): rs rim: R2 = 0.12, RMSE = 199 s m−1, p < 0.05; rs centre: R2 = 0.17, RMSE = 152 s m−1, p < 0.01, n = 46 both).

rscentre = 169.9 + 0.67(Rn); rsrim = 680.1

4.3(RH )

recorded by the chamber IRGA in 2015 (> 45%). For the 2014 campaign, non-significant dCH2O/dt slopes were only recorded during the two 24-hour measurement campaigns (covering only 8% of measurements), where negligible ET rates are expected due to low levels of net radiation at night-time. Despite employing the same methodology at the same measurement plots, the 2015 data displayed no such diurnal dependence of significant dCH2O/dt slopes. The high number of nonsignificant slopes here highlight the poor performance of the chamber method in the cool, moist conditions which were dominant during the second field-campaign, leading us to question its robustness for measuring ET in such a low-flux environment. This contrasting performance of the chamber method under different hydrometeorological conditions has not been identified in the experiments at Barrow (Raz-Yaseef et al., 2017; Young-Robertson et al., 2018), most likely due to limited temporal coverage. Long-term chamber ET measurements are required across a range of sites to further assess this issue.

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4.2.2. Upscaling ET Flux estimates for the 2014 campaign from our chamber-based upscaling model (Eq. (4)) are presented alongside EC-based estimates in Fig. 7. The ET flux is clearly underestimated by our preliminary upscaling model (Fig. 7a), with average modelled ET rates for the campaign period around a factor of two smaller than EC-derived estimates (ETupscaled = 0.74 ± 0.74 mm d−1; ETEC =1.32 ± 1.27 mm d−1; model R2 = 0.79, p < 0.01). Although the daily cycle was often captured by this model, approximately 20% of the observed variability remained unexplained. Daily peaks in ET measured by the EC technique were considerably larger than those predicted by the upscaling model. When ETeddy was high, our preliminary upscaling model therefore had a strong tendency to under-predict ET, producing what we term a positive ETresidual (ETresidual = ETeddy ETupscaled ). We used linear regression to investigate which factors affected the performance of our preliminary upscaling model, finding that meteorological conditions were able to explain a significant amount of the gap between upscaled and EC estimates (p < 0.01). Rn exhibited the strongest influence on ETresidual (R2 = 0.58, p < 0.01), with a strong positive correlation between the two variables. During periods with high levels of net radiation, our upscaling model under-predicted ET rates by over 3 mm d−1. In addition to Rn, analysis found that humidity and temperature could each explain around 20% of the residual, with Tair and VPD positively influencing the size of the residual. Fig. 7b shows the relative ET rates for each of the footprint land cover components estimated by our upscaling model. Due to the large areal coverage of dry tundra inside the EC footprint, ET flux rates from this land cover type were weighted more heavily and therefore had a strong influence on the estimated flux rate. By comparing Fig. 7a and 7b, it is clear that chamber-based rates for both ETwet and ETdry were much lower than ETeddy, whereas estimates of ETwater were of similar magnitude to ETeddy. Using a linear regression with intercept forced through the origin, we therefore calculated a correction factor (CF) of 1.92 which was applied to the wet and dry tundra ET components of our upscaling model. This CF is in addition to our tube length correction (see Section 3.1.1.2) and reduced the gap between average ETeddy and estimates by over 50% (ETresidual average ETupscaled = 0.69 ± 0.70 mm d−1; ETresidual after CF applied = 0.29 ± 0.57 mm d−1).

5.1.2. Chamber ET fluxes A systematic underestimation of fluxes is a key problem affecting chamber estimates. This methodological bias results from a number of factors, including: (1) alteration of the water vapour concentration gradient upon chamber setting (Davidson et al., 2002; Raz-Yaseef et al., 2010); and (2) modification of the microclimate by the chamber (Davidson et al., 2002; Livingston and Hutchinson, 1995). In terms of the second point, during chamber deployment the studied surface and atmosphere are effectively decoupled, especially in terms of wind speed, which is known to be a strong control on ET (e.g. Lafleur, 2008). Interestingly however, we found the residual between our upscaling model and the EC data displayed only a weak relationship with wind speed (R2 = 0.05, p < 0.01) despite the fact that fan-induced wind speeds inside the chamber were over a factor of ten lower than those measured in the ambient environment, influencing EC flux estimates. Finally, it is important to note that water vapour transport as ET is different from the diffusive flux of CO2 or CH4 measured in other chamber experiments due to the phase change of liquid water to water vapour. The sorption and desorption of water molecules between the chamber and tubing is important to consider for chamber flux estimates, especially when the IRGA follows sample transport through a tube, and given the stickiness of water vapour compared with CO2 or CH4. In our study, during times which were especially conducive to ET (e.g. high levels of incoming solar radiation, large VPD, moist surface), we observed condensation on the inside of the chamber over longer periods of chamber deployment. In contrast, no visible condensation was mentioned for either the portable or automatic chamber setups at Barrow (Cohen et al., 2015; Raz-Yaseef et al., 2017; Young-Robertson et al., 2018), although sorption is still likely to have had an impact on ET estimates. We hypothesise that sorption could also have caused our chamber-based approach to underestimate ET from wet sites relatively more than from dry sites. The higher ET rates from wet tundra areas create moister air, which could have led to more condensation at the tube and chamber walls than at dry sites, and in turn could have caused a higher relative flux underestimation at this microsite. The lower average summer temperatures during the Barrow measurement campaigns, and the relatively dry location of their automated chamber may explain some of the difference seen in the present study. Although the problem of sorption and desorption has been considered for closed-path EC systems, to our knowledge little attention has been paid to this issue in chamber setups. Recent EC studies examining the extent of tube attenuation of water vapour fluctuations find this is influenced by meteorological conditions, especially RH and temperature (e.g. Haslwanter et al., 2009; Ibrom et al., 2007; Nordbo et al., 2013; Runkle et al., 2012), which is consistent with our contrasting dCH2O/dt slope characteristics between measurement campaigns. The impact and extent of attenuation could be lessened by warming the sampling tube and significantly reducing its length. The former may

5. Discussion 5.1. Spatial and temporal variability of ET 5.1.1. Sensitivity to hydrometeorological conditions Our analysis has highlighted the sensitivity of chamber-based ET fluxes to the predominant hydrometeorological conditions. We identified large differences in the magnitude of chamber ET fluxes between the two campaign years; with ETchamber in the relatively dry year of 2014 being around five times higher than observed during the wetter campaign of 2015. A strong interannual variability in ET has been previously reported across arctic sites (Liljedahl et al., 2011; Lund et al., 2014; Rouse et al., 1992; Wu et al., 2010; Young-Robertson et al., 2018). However, in the present study, it appears remarkable that estimates from the EC tower are much less sensitive to the changing conditions than the chamber measurements, with a considerably smaller difference in average flux rate (30%) seen between the two years. The observed contrast in hydrometeorological conditions could also (in part) explain the high number of non-significant dCH2O/dt slopes 9

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have been observed indirectly during the 2014 campaign when black tubing was used and a much higher proportion of significant dCH2O/dt fluxes was observed than during the 2015 campaign where transparent tubing was used. These are aspects of research design which should be examined closely in future studies using the chamber method to measure ET fluxes.

both Langer et al. (2011) and Muster et al. (2012), who report ET fluxes from wet tundra to be around a factor of two higher than those from dry tundra (see Table 2). These differences are likely a result of interannual variability and spatial differences within wet and dry tundra areas, as our chamber data also show considerable overlap in ET rates between rim and centre microsites at our low-centred polygon. ET rates in the literature for arctic tundra sites vary considerably, but are in broad agreement with our findings. Blok et al. (2011) for example report summer ET rates of 0.7–2.4 mm d−1 over a two week period at a more easterly Siberian tundra site using EC and lysimetry. Considering studies with longer timeseries, for a site in northeast Greenland, Lund et al. (2014) calculate 10-year mean midday ET rate of 1.8 ± 0.2 mm d−1, and Liljedahl et al. (2011) calculate a 5-year mean of 1.5 mm d−1 at Barrow, Alaska. For our upscaling analysis we assumed that our local estimates of ETwater, ETwet, and ETdry were fully representative of all such areas inside the EC footprint, whereas in reality they are likely to vary. Furthermore, it is important to highlight that no direct measurements of ETwater were made during this study, and that this category grouped overgrown- and open water areas, which will differ in their respective ET rates. Over the 2014 campaign period, we found a strong agreement between EC and modelled ET estimates from our upscaling analysis (R2 = 0.82, RMSE = 0.54 mm d−1), although both chamber and upscaled estimates were consistently smaller than their EC counterparts. Raz-Yaseef et al. (2017) are the only authors we know of to have compared ET rates across scales (between chamber and EC measurements). ET rates measured by their automatic chamber were on average 44% of those measured by an EC system. As no upscaling analysis was performed, the authors assumed this disparity was linked to the contrasting plant types and geomorphological units covered by the two systems. In our study, after accounting for differing spatial coverage in our upscaling analysis and applying corrections for tube length and chamber flux underestimation, we see the size of this discrepancy reduced by more than half to around 18%. To put this into perspective, studies scaling CO2 fluxes from the chamber to ecosystem scale in cold environments, find a much larger discrepancy ranging from 25 to 60% (Fox et al., 2008; Stoy et al., 2013). Our ETresidual likely results from underestimation by the chamber method, and challenges upscaling over areas of heterogeneous land cover. Furthermore, we compared chamber estimates, i.e. point flux measurements collected over a period of seconds across a single low-centred polygon (length 10 m); with EC measurements taken over a 30-minute averaging period, representative of a much larger area (1,000 m2). This mismatch is a source of systematic uncertainty in our chamber-based estimates for wet and dry tundra that were used to drive the upscaling model. For our 2014 data, applying a correction factor to our wet and dry tundra ET rates substantially improved agreement between EC and upscaled estimates (by > 50%). Although this approach is commonpractise in the literature (McLeod et al., 2004; Raz-Yaseef et al., 2010), for our study the sensitivity of the chamber flux errors to changing light and moisture conditions means that we would require a different CF for each campaign. In contrast, Cohen et al. (2015) reported that only one calibration is needed, after which the chamber can be deployed in a range of ecosystems; with their CF holding in both semi-arid and continuous permafrost environments. Possible reasons for the contrasting results found in our study are: the shorter sampling tube employed by Cohen et al. (2015), which would have reduced attenuation effects; differences in IRGA performance; and their measurement of ET at only a high-centred polygon. As this microsite is comparatively drier than our wet tundra, we hypothesise that lower ET rates and air moisture from this microsite would again have reduced the effect of sorption on the authors’ flux estimates. In our study, the need to calculate a site-specific and temporally varying correction factor means that the chamber method cannot be used as a stand-alone tool for measuring ET.

5.1.3. Eddy covariance ET fluxes At the ecosystem level, we employed EC estimates as a benchmark against which to evaluate the performance of the chamber method, hence our analysis assumes that these tower-based estimates are correct. In reality, EC measurements are associated with error arising for example from data processing steps and gap-filling (Falge et al., 2001; Papale et al., 2006), inhomogeneity of the footprint area, and micrometeorological problems such as an incomplete energy balance closure (Foken, 2008). As such, the EC method is known to under-capture fluxes, and true ecosystem-level flux rates are likely to be greater than reported here. Our analysis also relied heavily on the accuracy of our chosen footprint model (Kormann and Meixner, 2001) and the landcover classification map (Boike et al., 2012). A shift in the size and shape of the flux source area would impact our results, especially during periods when larger water-covered areas are involved. 5.2. Performance of the chamber method using EC as a benchmark 5.2.1. The Penman-Monteith model Adjusting the P–M model to the chamber data produced implausibly high values for rs (> 200 s m−1), whereas tuning the model to the EC data produced estimates for rs (62 s m−1) which were in much closer agreement with that previously reported for the footprint (44 s m−1, Kutzbach 2006), and for other permafrost areas including sites in Alaska (175 s m−1, Beringer et al. 2005) and Sweden (160 ± 70 s m−1, Kellner, 2001), although there appears to be considerable spread among estimates. To examine the contribution of the chamber data to the success of the P-M model in predicting ET, we tested its performance using fixed rs values from the above literature. Our comparison found that running the P-M model independently of the chamber results produced ET estimates which were in much better agreement with those from the literature and our tower-based EC estimates in terms of magnitude. For example, for the 2014 campaign running the P-M model with Kellner’s rs value (Kellner, 2001) produces ET estimates of 1.8 ± 1.3 mm d−1 for the polygon centre, and 1.6 ± 1.5 mm d−1 for polygon rim; which would reduce the need for any correction factor. This finding suggests that the P-M model alone is able to provide ET estimates which are much closer to our EC benchmark than the chamber method. As such, despite having demonstrated that the chamber method can tease apart differences in microsite conditions, we argue that the chamber method alone is not appropriate for measuring fluxes in wet, cool tundra climates such as our study site. 5.2.2. Upscaling ET For the 2014 campaign our ground-based ET estimates (corrected for tube length) were successfully upscaled after application of a CF. For the polygonal tundra, our ETupscaled is in good agreement with flux rates from the tower-based EC system and those in the literature for Samoylov Island (Table 2). Examining the individual land cover components, our ET estimates for water-covered areas (2.4 ± 1.8 mm d−1) fall within the upper range reported in the available literature (Abnizova et al., 2012; Muster et al., 2012). Our modelled ET rates for wet and dry tundra are lower than reported previously for Samoylov Island. Most notably, our summer ET rates for wet tundra are around 50–60% lower than that reported at the site using lysimetry (Muster et al., 2012) and mobile EC towers (Langer et al., 2011). Our upscaling model also predicts similar ET rates for wet and dry tundra areas (0.9 mm d−1 and 0.7 mm d−1 respectively), which is in stark contrast to 10

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Table 2 ET rate estimates from the literature for the polygonal tundra landscape on Samoylov Island shown alongside those from the present study. ET rate is given as mean value ± SD where possible, and as a single mean value, range, or maximum in other cases. Methods are: closed chamber (CC), flux gradient approach (FG), lysimetry (LY), eddy covariance (EC), upscaling (UP), and water balance (WB). Note: chamber ET estimates from the present study have been corrected to simulate a 1-m sample tube (Section 3.1.1.2), and an additional CF has been applied to upscaling estimates as described in Section 4.2.2. For wet and dry tundra estimates from the present study, we show flux rates from our polygon rim (dry tundra) and -centre (wet tundra) chamber measurements (CC) and also, in brackets, the ET rates used in our upscaling model for wet and dry tundra with the CF applied (UP). ET rate (mm d−1)

Study

The present study

Wet tundra

Dry tundra

1.2 ± 0.7 (0.9 ± 1.7)

0.9 (0.7 1.1 1.3 1.0

± ± ± ± ±

Study period

Method

2.4 ± 1.8 –

Summer 2014 (Jul–Aug)

1.4 ± 0.7

Summer 2008 (Jul–Aug) Summer 2008 (Jun–Aug) Summer 2008 (Jun–Aug) Summer 2011 (Jun–Aug)

CC, FG (UP) UP EC LY, FG

Water bodies

0.7 1.5) 1.4* 1.3* 0.7

Muster et al. (2012)

1.8 ± 1.0

Langer et al. (2011)

2.3 ± 1.2

1.0 ± 1.1

–

–

–

1.7

Abnizova et al. (2012) Helbig et al. (2013)

1.6 (average)* 3.0 (max)*

EC WB WB

*Denoting that those selections refer to all types of tundra.

6. Conclusion

improvement and careful evaluation is required before this technique can be reliably used as a stand-alone tool to estimate ET. In the meantime, we have shown that when conducted alongside a trusted benchmark such as EC, the chamber method can be used to capture valuable information on small-scale heterogeneity in ET. In such a setup, the benchmark allows for the appropriate CF to be calculated depending on the dominant hydrometeorological conditions. Furthermore, as water vapour data is routinely collected during CO2 and CH4 chamber flux experiments, generating a parallel series of ET measurements, this would be a feasible approach to greatly improve monitoring efforts of ET across the Arctic, and would be of substantial benefit to the modelling community.

By comparing ground-based chamber and tower-based EC estimates of ET, our study has built on the work conducted at Barrow, Alaska, examining the suitability of chambers for measuring ET in a low water flux, permafrost environment (Cohen et al., 2015; Raz-Yaseef et al., 2017; Young-Robertson et al., 2018). Performing chamber measurements over two complete summer seasons with contrasting hydrometeorological conditions, and conducting an upscaling analysis to directly compare against EC measurements allowed us to carry out a more thorough temporal evaluation of chamber ET estimates than has been done previously. With our chamber setup we had two main findings: (1) a clear heterogeneity in ET exists at the small-scale – both spatially in terms of our rim and centre microsites, and temporally with a strong contrast in chamber-measured ET over our two measurement campaigns; and (2) good agreement between upscaled and EC rates (R2 = 0.82) can be achieved after applying a CF to our modelled ET rates for wet and dry tundra. However, our study has highlighted a number of issues surrounding the use of the chamber technique as a stand-alone tool to measure ET in arctic wetland environments, namely:

CRediT authorship contribution statement Gillian Simpson: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft, Writing - review & editing. Benjamin R.K. Runkle: Conceptualization, Supervision, Writing - review & editing, Project administration. Tim Eckhardt: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Writing - review & editing. Lars Kutzbach: Conceptualization, Funding acquisition, Supervision, Writing - review & editing, Resources, Project administration.

• A sizeable underestimation of ET fluxes by the chamber method com-

•

pared to EC estimates. Without a CF, average ET estimates produced by our upscaling model for wet and dry tundra areas were considerably smaller (> 50%) than reported in the literature for the same site. As with chamber-measured ET, our upscaling estimates were also consistently lower than EC estimates. We suggest this is predominantly due to attenuation affects in the sampling tube connecting the chamber to the IRGA. The magnitude of underestimation appears to be greater under more moist conditions – for example in areas of wet tundra, where higher ET rates can be expected than at the drier polygon rims. A changing sensitivity of chamber flux estimates to the predominant hydrometeorological conditions. We observed that differences in the magnitude of ET fluxes between a wet and dry year campaign periods were much greater in the chamber data (80% difference) than in the EC data (20% difference).

Acknowledgements This research was supported by the German Ministry of Education and Research (CarboPerm-Project, BMBF Grant No. 03G0836A; and the KoPf-Project, BMBF Grant No. 03F0764A) and through the Cluster of Excellence “CliSAP” (EXC177) at the University of Hamburg, funded by the German Research Foundation (DFG). The authors thank the organisers and participants of the LENA 2014, LENA 2015, and LENA 2018 expeditions, especially M.N. Grigoriev (Permafrost Institute, Yakutsk), G. Stoof, V. Assmann, and W. Schneider (Alfred Wegener Institute, Potsdam) for their logistical support. We are also grateful to Christian Wille for processing the EC data and conducting the subsequent footprint analyses. Finally, we thank the two anonymous reviewers and the JoH Editorial team for their helpful comments and ideas which have improved this manuscript.

Despite the success of chambers for measuring ET at Barrow, Alaska, our study highlights large differences in the performance of the chamber method depending on experimental setup, site characteristics, and hydrometeorological conditions. We suggest that in cool, wet climatic conditions, such as those in the arctic tundra, significant methodological

Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jhydrol.2019.124030. 11

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