Evaluation of a plot scale methane emission model at the ecosystem scale using eddy covariance observations and footprint modelling

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Introduction
Northern processes that depend on many factors such as water table position, soil temperature, substrate availability, vegetation composition etc. (Shurpali et al., 1993;Bubier, 1995;Kim et al., 1998;Ström et al., 2003;Kutzbach et al., 2004;Wille et al., 2008).In continuous permafrost environments these factors are additionally affected by thawing and freezing of permafrost (Turetsky et al., 2002;Christensen et al., 2004).This complexity imposes difficulties on the quantification of methane emissions and increases the uncertainty of methane budget estimations.The use of widespread techniques such as the closed-chamber method and eddy covariance (EC) yields measurements over limited areas, therefore it is necessary to produce reliable models for an accurate methane budget estimation over larger areas through upscaling.
Several process-based and empirical models were developed during the last two decades to achieve that goal (Cao et al., 1996;Christensen et al., 1996;Bekki and Law, 1997;Walter et al., 2001;Zhuang et al., 2004;van Huissteden et al., 2006;Wania et al., 2010;Zhang et al., 2012).Two methods are usually employed to validate these models: (i) comparison with data, acquired with the closed-chamber method and (ii) weighing of the flux of each vegetation class in the typical fetch area of the EC tower by its areal fraction and comparison to the EC measurements.
Studies using the first method for validation rely on datasets collected with the closed-chamber method.It was shown however (Kellner et al., 2006;Sachs et al., 2008) that measurements with closed-chambers are affected by several problems such as a low spatial representativeness, low temporal resolution, inability to register transport of methane by ebullition events etc., which all increase the uncertainty of the measurements.One extreme example of low spatial representativeness was shown by Simpson et al. (1997), who discovered that an Aspen site previously reported as a methane sink, according to the data collected with closed-chambers, was actually a methane source when measured with an eddy covariance system.In turn, a way to avoid low temporal resolution is the use of automatic chambers.For the measurements to be representative of the site, many such devices should be installed, which increases Introduction

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Full the costs not only in monetary terms, but also in terms of logistics and power supply.The latter two factors are important for remote sites.
The second method solves these problems with the use of the EC technique, which typically has a high frequency of measurements (half-hourly flux calculations are becoming a de facto standard), large coverage (e.g.several hectares), and which is also a non-intrusive way of measuring ecosystem scale fluxes (Aubinet, 2000;Baldocchi, 2003;Aubinet et al., 2012).In many of the studies using this method, however, vegetation composition in the EC tower footprint is not taken into account and only areal fractions of different vegetation types of the whole area are used for upscaling (Hendriks et al., 2008;Sachs et al., 2010;Zhang et al., 2012).Significant mismatches may be introduced for sites with a heterogeneous and irregular vegetation pattern when doing so, for example when the EC footprint area is covering only one or a limited group of vegetation types, leaving emissions from other types unaccounted for.
In this study, we present an evaluation of the PEATLAND-VU methane emission model using EC observations collected at the Kytalyk research station in northeastern Siberia, Russia.We weigh each unit source area on a high-resolution vegetation map in the fetch of the EC tower by its contribution to the total flux (so called 'footprint function' or "source weight function") with subsequent integration.This method benefits from advantages of the EC technique and also takes into account the vegetation composition in the EC tower footprint.
The objectives of this study are: (1) to evaluate the simulation of methane emissions from the PEATLAND-VU model with eddy covariance measurements from an ecosystem with heterogeneous vegetation, while using a footprint-weighted average upscaling approach on a daily time scale, and (2) to compare the area-weighted average to the footprint-weighted average upscaling approach.Introduction

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Validation site
The validation site "Kytalyk" is located in an oligotrophic tundra area in the Indigirka lowlands, Russia (70.82 • N, 147.50 • E) (Fig. 1).The area is underlain by continuous ice-rich permafrost.Microrelief at the surface consists of high-and low-centered ice wedge polygons and low palsas (ice-rich mounds).The typical active layer depth during summer ranges from around 10 cm in dry areas to 50 cm in wet areas.The station is situated in a drained thaw lake basin (van der Molen et al., 2007).Based on the development of the vegetation and periglacial features it can be classified as ancient (Hinkel et al., 2003), suggesting drainage at least several thousand years ago.The landscape is relatively flat, except for yedoma ridges situated approximately 400 m to the west and east of the EC tower.
The vegetation consists primarily of Betula nana, Eriophorum angustifolium, Eriophorum vaginatum, Salix pulchra and Sphagnum sp. on the terrace.To the west and southwest of the station there's a meander of the Berelekh river (lower course is also known as "Yelon") with a floodplain, dominated by Arctophila fulva and Arctagrostis latifolia grasses with patches of Salix pulchra on the floodplain and on the levees.The typical vegetation height on the terrace ranges from 10 to 50 cm and some Salix pulchra shrubs may reach 2 m on the levees along the river.
The eddy covariance tower together with a meteo tower is installed approximately 200 m to the north of the research station.

Measurements of environmental parameters
A set of environmental parameters was measured by the meteorological tower installed 5 m away from the EC tower.

Eddy covariance measurements
An ultrasonic anemometer (Gill Instruments, Lymington, UK, type R3-50) was used to measure wind speed and temperature, while a closed-path fast methane analyzer (Los Gatos Research, Mountain View, California, USA, type DLT-100) was used for the fast methane measurements.These measurements were conducted from 25 July till 9 August in 2008 and from 5 July till 4 August in 2009.The instruments were installed at a height (z m ) of 4.7 m.The air intake was situated 20 cm from the anemometer and leveled at approximately the same level as the anemometer's central point.
At the other end of the tube, a vacuum scroll pump (XDS35i, BOC Edwards, Crawly, UK) was installed.The fluxes were calculated following the EUROFLUX methodology (Aubinet, 2000) with the addition of the angle of attack correction by Nakai et al. (2006).For a detailed description of the EC setup and flux calculation we refer to Parmentier et al. (2011).
As reported by Parmentier et al. (2011), the EC measurements during periods when the wind is blowing from the direction of the research station (155 • > α < 195 • ) might be disturbed.Since there are no artificial methane sinks any disturbance would lead to elevated emissions.However we did not find a significant difference in magnitude of concentrations and fluxes coming from this wind sector.Moreover, all the potential artificial methane sources are point-like and located at the distance of at least 200 m, therefore the footprint function value for these locations would be very small decreasing the artifical component of the measured fluxes.Thus, the screening for this wind sector was not applied.Introduction

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Full An energy balance analysis of the system was done by van der Molen et al. ( 2007) and Parmentier et al. (2011), who found a good energy balance closure.
Figure 3 shows frequency binned and averaged co-spectra of sensible heat and methane fluxes for both measurement periods.The inertial sub-range decays according to a −4/3 power law as expected.As described by Hendriks et al. (2008), there is an underestimation of the normalised co-spectral density of the methane fluxes in the high frequency range and an overestimation in the lower range, which is a characteristic of the system.
The good energy balance closure and expected shapes of co-spectral densities of sensible heat and methane fluxes indicate that the calculated fluxes are of high quality and can be used as a reference in our analysis.
Since the PEATLAND-VU model works only on a daily time scale, half-hourly measurements were downsampled to daily averages, if more than 35 measurements were available (∼73 % or more measurements) per day.Using this method for averaging, 5 days were discarded in total -25, 28 July and 9 August in 2008 and 5 July and 4 August in 2009.

Closed-chamber measurements
Measurements of methane fluxes with the manual closed-chamber technique were conducted from 31 July until 7 August in 2008 and 11 July until 1 August in 2009.A round transparent chamber and a photo-acoustic gas monitor (model 1312, Innova AirTech Instruments, Ballerup, Denmark) were used to measure methane concentrations in the chamber with an interval of 2 min and a total duration of 8 min.The chamber was shielded from the sun during the measurements to prevent large temperature increases.A linear regression was then applied to this data to calculate the methane fluxes.A quality check according to van Huissteden et al. (2005) was performed and data which showed chamber leakage or ebullition spikes were discarded.Together with the flux measurements, soil temperature at 10 cm depth, active layer depth and water table position were recorded.Introduction

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Full  2005), a reclassification of the vegetation map was done to match the model's output (see Table 1).An Arctophila fulva and Arctagrostis latifolia class was divided into two different subclasses -TW1 on the terrace and FW2 on the floodplain (Fig. 4).As it was shown previously (van Huissteden et al., 2005), the floodplain may have significantly higher methane emissions, therefore using either the TW1 or FW2 class for both terrace and floodplain would yield biased results.
The river and lakes class, which comprise nearly 6 % of the map, had no measurements of methane fluxes.There are very few studies that provide some insight into methane emissions from Arctic rivers.One study (Kling et al., 1992) reported 0.36 mmol m −2 d −1 (0.24 mg CH 4 m −2 h −1 ) from Kaparuk river, Alaska, USA.A recent study by Striegl et al. (2012) showed a similar emission estimate of 0.39 mmol m −2 d −1 (0.26 mg CH 4 m −2 h −1 ) from the middle course (Arctic part) of the Yukon river, Alaska, USA.We take an average of 0.375 mmol m −2 d −1 (0.25 mg CH 4 m −2 h −1 ) and assume this emission as constant for the class throughout the EC measurement periods for both years.The error introduced by this assumption is bound to be small as this class only covers 6 % of the map.
There are only 2 % of the pixels on the map left unclassified due to cloud cover in the original image, which has only a minor impact on the final figure.
It is important to notice, that high-resolution mapping of such ecosystems is essential for upscaling methane fluxes with any upscaling method (Riutta et al., 2007;Schrier-Uijl et al., 2010;Sachs et al., 2010).Introduction

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PEATLAND-VU model description
The latest version of the PEATLAND-VU model was used in this study (van Huissteden et al., 2006).It is a process-based, plot-scale model developed for the quantification of CO 2 and CH 4 fluxes from wetlands.A CH 4 sub-model is implemented following Walter and Heimann (2000) for production, oxidation and transport of methane, and for addition of organic material to the root zone.Changes to the model include the addition of a water table model to calculate water table from precipitation and evaporation (Granberg et al., 1999;Yurova et al., 2007) and a photosynthesis module for the estimation of gross primary production (GPP) (Haxeltine et al., 1996;Sitch et al., 2003).
The model was calibrated with data collected with the closed-chamber method during

Footprint model description
For modelling of the EC tower footprints we used the model by Kormann and Meixner (2001) (Eq.1).similarity theory, which only holds in the atmospheric stability regimes defined by −1 < ς < +1 (where ς = z m /L, and L the Monin-Obukhov length), we only used flux data and footprint estimations of periods characterized by this stability regime in our analysis.The 2-D footprint estimations provided by the model on an half-hourly basis were then averaged to get daily means.After all mentioned quality screenings, 40 daily means were available for further analysis.
Mean daily 2-D footprint estimates were integrated within the domain defined by the available vegetation map.It was found that 56 % of the time the integral (Ω) exceeded the value of 0.85 -with a minimum of 0.78 -suggesting that the vegetation map is large enough for the purposes of our analysis.
Figure 5 shows flux footprint climatology of the EC tower.The peak value of footprint function typically stays at a ∼51 m distance from the tower and average 80 % cumulative flux distance is ∼377 m.The peak location and the 80 % cumulative flux distance are distributed evenly across the wind directions, except the S-E direction where the 80 % cumulative flux distance extends to more than 500 m.
For the footprint model to be applicable to the site, it's necessary to assume that while the vegetation pattern is heterogeneous, the surface is relatively homogeneous in terms of vegetation height, so that momentum flux is not disturbed (Forbrich et al., 2011).Considering the vegetation height of the site and the measurement height of EC system, we assume this disturbance to be negligible.

Upscaling
To compare fluxes modelled for the different classes (TD1, TW1, TW4, FW2 and River and lakes in this study) to the EC flux, two upscaling methods were applied.

Area-weighted average
Upscaling with the area-weighted (AW) average was done by calculating the areal fractions of the vegetation types on the vegetation map and multiplying them by the emis-Introduction

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Full sion factor for that class.Subsequently, these area-weighted emissions were summed (Eq.2).
where F AW is the ecosystem scale methane flux, f i the fraction on the map (Table 1), and F i the methane emission of the class.Numbers i = 1 − 5 indicate classes TD1, TW1, TW4, FW2, and River and Lakes.This method was used in numerous studies in the past and is considered to yield a good match on a monthly to seasonal scale (van der Molen et al., 2007;Parmentier et al., 2011;Zhang et al., 2012).

Footprint-weighted average
To derive a quantity that could be directly compared to measured EC fluxes, we calculated the ecosystem scale methane emissions with the footprint-weighted (FW) average, after substitution of classes with their respective emission factors on the vegetation map.Every pixel on this methane emissions map was then weighted with the daily averaged 2-D footprint function.Subsequently, weighted emission factors from every pixel were summed.To compensate for a limitation of footprint functions by the vegetation map (Ω < 1 situations), the fluxes were normalized by Ω (Eq.3).This operation was needed to make individual flux estimates directly comparable, and it assumes that the vegetation pattern outside the vegetation map does not differ significantly from the mapped one.
where (x, y), A res the area of a pixel and X, Y the dimensions of the vegetation map.A similar approach was used by Forbrich et al. (2011), but for downscaling instead of upscaling.

Similarity between the upscaling methods
The two upscaling methods -even though different -share some similarities.As modelled footprints cover only a portion of a vegetation map, one may think of the FW method as a subset of the AW method, which covers the whole vegetation map.In this regard, it is possible to imagine the AW method as the FW method with a footprint of constant shape, such that the contribution of each vegetation type equals the fractional area of these types on a vegetation map.
To show this, we calculate the difference between the fluxes upscaled by the two methods (Eq.4) and the "vegetation composition difference" (Eq.5), which shows the sum of absolute differences between the total footprint function of the emitting class normalised by Ω and fractional cover of that class on the vegetation map.

PEATLAND-VU methane emissions modelling results
Figure explained by a small change in soil temperature and water table (by results of multilinear regression analysis -r 2 = 0.64 and 0.56 for TW1 and TW4 respectively).FW2 shows a strong relationship with soil temperature (r 2 = 0.68), which slightly improves when water table is added as another predictor (r 2 = 0.73).The model shows a poor fit to the chamber measurements in 2008 (Fig. 6).The model failed to reproduce the temporal patterns and the magnitude of the emissions from the TW4 and FW2 classes.
There are not enough measurements from the TW1 class to draw conclusions on the temporal pattern, although the existing data suggests an underestimation of fluxes by nearly 20 %.In 2009, the model follows the chamber measurements much closer.Most of the time, the modelled fluxes stay within one standard deviation from the observed values.

Upscaling by AW average
Figure 7a shows a comparison of the methane flux modelled with the PEATLAND-VU model and upscaled using the AW approach and the EC flux observations.Using the AW method to upscale the modelled fluxes yields a low correlation with the EC flux on a daily time scale with r 2 = 0.14.The upscaled fluxes do not follow the temporal pattern observed by the EC system, having little variation throughout both seasons.
The magnitude of the upscaled flux is noticeably lower than the EC flux.Cumulative modelled emissions during the EC measurement periods in 2008 and 2009 are 0.53 and 0.87 g CH 4 m −2 respectively, while the cumulative EC flux shows 0.83 and 1.09 g CH 4 m −2 respectively for the same periods.Thus, the AW upscaling approach underestimates methane emissions by 27.4 % when compared to the EC flux.There were only 8 days in total where chamber measurements were performed on all of the relevant vegetation types during the same day.Daily averages of these measurements were upscaled using the same technique.The magnitude of the upscaled fluxes is close to the EC flux.Introduction

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Upscaling by FW average
The methane flux modelled with the PEATLAND-VU model and upscaled using the FW approach shows a good correlation with the EC flux with r 2 = 0.7 (Fig. 7b).The magnitude of the upscaled flux is similar to the EC flux.More importantly, however, it also follows the temporal pattern of the EC flux.The cumulative flux for 2008 and 2009 is 0.84 and 1.3 g CH 4 m −2 respectively, while the cumulative EC flux shows 0.83 and 1.09 g CH 4 m −2 respectively for the same periods.Therefore, using the FW upscaling approach yields slightly higher (+11.4%) methane emissions as compared to the EC observations.The upscaled fluxes from the chamber measurements are significantly higher than the EC flux in 2008, but not in 2009.

Similarity between the upscaling methods
The difference between the two upscaling methods becomes minimal, when the "vegetation composition difference" is low (Fig. 8).At our validation site, the occurence of (nearly) non-emitting vegetation types is high.Therefore more often than not limiting the contribution area to EC footprint extent leads to a change in vegetation composition, such that the contribution of high-emitting vegetation types is higher.This in turn leads to elevated values of upscaled fluxes.In some cases, however, when the contribution of vegetation types as calculated by the footprint model roughly matches the fractional areas of these types on the vegetation map, the upscaled fluxes by both methods and the EC flux match as well.This only happened when the EC flux was low, suggesting a high contribution of non-emitting types.On larger scales (i.e. when a vegetation map extent is much larger than a typical footprint of an EC tower) this similarity may not hold, as environmental parameters and vegetation become latitude-and longitude-dependent.Introduction

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Upscaling by AW average
The magnitude of the modelled CH 4 flux upscaled by the AW approach was ∼27 % lower than the magnitude of the EC observations.The temporal dynamics were also not captured, leading to a low correlation with the EC flux.To explain this, we calculated the contribution of each class to the total upscaled flux.Cumulative contributions are presented on Fig. 9a.The TW1 class dominates the upscaled flux for both study periods, having 67 % contribution on average.TW4 and FW2 classes contribute nearly 15 % each.Variations in the upscaled flux could be explained largely by variations in the soil temperature at 4 cm depth and the water table (r 2 = 0.61).
Several reasons may have contributed to the low correlation with the EC flux.Firstly, as the vegetation composition in the EC tower footprint is not taken into account, significant mismatches can be introduced (Hargreaves and Fowler, 1998;Forbrich et al., 2011).In the case of a homogeneous and isotropic vegetation pattern, the consideration of a dynamic footprint will not lead to a significantly different flux.However, when the vegetation pattern is heterogeneous and moreover anisotropic (i.e. not the same in different directions as seen from the EC tower), a dynamic footprint will have a sizeable impact on the measured flux.A changing vegetation composition in the EC tower footprint will change the influence of different vegetation types on the EC flux.The importance of the vegetation distribution (Shannon et al., 1996) and the EC flux footprint consideration (Leclerc and Thurtell, 1990) were recognized almost two decades ago.The latter, however, is still not properly considered, when comparing modelled methane fluxes with EC observations.To mitigate this issue, we present a validation approach, which considers both these phenomena in the next sub-section.
Secondly, as it was shown previously (Becker et al., 2008;Forbrich et al., 2011), the vegetation map classification may contribute to the mismatches.Since the resolution of the map is 2 m, it is possible that some of the classes might have been overor underestimated.From field observations, however, it is likely that this inaccuracy Introduction

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Full will explain only a small portion of the mismatch.Betula nana dwarf shrubs and Salix bushes that form the TD1 class tend to grow in patches on palsa-like formations with radii (linear sizes) larger than 2 m.Therefore, only borders of these patches will be uncertain, largely preserving the surface area of this class.Grasses that comprise the TW1 class have a similar growth pattern, but usually grow in wetter areas.The FW2 class is localized on the floodplain, and consists of large contiguous areas (Fig. 4).It forms two large patches, separated by the small creek with patches of Salix pulchra.
We use this class for the whole wet area on the floodplain, despite apparently heterogeneous vegetation.This may not be realistic as van Huissteden et al. (2005) reported high spatial variability of methane fluxes from that area.Hotspots of methane emissions that cannot be recognized on the satellite image due to the limited resolution may exist.Significant uncertainty may also exist in the TW4 class classification, which has a highly heterogeneous pattern.Parmentier et al. (2011) made an assessment of the accuracy of the vegetation map and report 86.79 % accuracy.This accuracy provides us with confidence, that even with a higher resolution, the distribution of different vegetation types on the map will be largely preserved.Thirdly, PEATLAND-VU model errors could be a reason for the mismatch.van Huissteden et al. ( 2009) conducted a model sensitivity analysis with the Generalized Likelihood Uncertainty Estimation (GLUE) method, showing that the model performs noticeably better for the terrace and somewhat worse for the floodplain.Together with the neglected hot spots on the floodplain, it is possible that the emissions from the floodplain are significantly different from the values reported by the model.Another source of error is the lack of spatial variation of the environmental parameters in the model.Currently, the same temperature and water table position measurements, for example, are used to model CH 4 fluxes from different classes.In reality, some variation in these parameters exist within each class which is not accounted for in the model.This effect would be more pronounced on a plot scale and will diminish on the ecosystem scale.

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Upscaling by FW average
Upscaled methane fluxes with the FW approach showed a good correlation with the EC measurements on a daily time scale with r 2 = 0.7 (Fig. 7b).Cumulative contributions are presented on Fig. 9b.Contributions from TW1, TW4 and FW2 generally follow the EC temporal pattern during both years.The TD1 class -comprising nearly half of the map (48.6 %) -and River and Lakes class -comprising almost 6 % -had nearly zero contributions throughout both measurement periods.In general, using this upscaling approach leads to a small (∼11.4 %) overestimation of the flux as compared to the EC flux.The upscaled flux was close to or within one standard deviation of the EC flux during both years, with a closer fit in 2009.
The magnitude of the three peaks in the EC measurements in 2008 on 29, 30 July, 1, 2 August, and 4 August was not reproduced.As mentioned earlier, this could be the result of model misperformance for the TW4 and FW2 classes as compared to the chamber measurements.The high water table position in 2008 could be the reason for this.Since the model failed to reproduce the magnitude of the fluxes, an underestimation occurs on these days.We hypothesize that a peak in modelled fluxes on 6 August is caused by a peak in soil temperature which occurred on that same day.Interestingly, this behaviour in ecosystem scale flux cannot be seen in the EC data on that day.The actual value is very close to one standard deviation of the EC flux however.
An explanation of this mismatch involves the two arguments about the vegetation map classification error and the PEATLAND-VU model performance, that are inherent to both upscaling methods.However, due to the addition of the footprint model to the upscaling scheme, the uncertainty of this model also needs to be considered.The exact uncertainty of the footprint model used in this study is unknown as no artificial tracer experiment was conducted to validate it.This is a general problem in footprint modelling (Schmid, 2002), because -as stated by Foken and Leclerc (2004) -artificial tracer validation experiments are too expensive.Many validation studies, therefore, compare analytical models to Lagrangian stochastic models, which are in turn compared to Large

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Full Eddy Simulation (LES) models (Foken and Leclerc, 2004).Another way is to use natural tracer experiments.In one such study (Neftel et al., 2008), it was shown that this particular footprint model performed quite well at a grassland site on the Central Swiss Plateau.Furthermore, the authors suggest that the model still produces useable, semiqualitative results under a broader range of the stability parameter (−3 < ς < +3) than used in this study.Considering the similar vegetation height (∼30 cm), we believe it is safe to assume that the model's result represents the "real" flux footprint quite closely.We showed that this approach is more favourable for the validation of methane emission models with EC observations rather than the AW approach on a daily time scale.Zhang et al. (2012) presented a study on the upscaling of methane fluxes based on the AW approach, and got a poor correlation with their EC measurements, even though they used a more than two times longer dataset for validation.We believe that one of the main reasons behind this mismatch may be the AW upscaling method used in the study.While no changes were made to the data and model configuration, using the FW method for upscaling improved the fit dramatically (from 0.14 to 0.7).Even though the FW approach requires a footprint model -which has it's own sources of uncertaintythis method allows useful insight into the performance of methane emission models.
In our study, the spatial resolution of the satellite image allowed for accurate mapping of highly heterogeneous vegetation patterns.The AW method, however, produced a cumulative methane emission estimate, which is ∼27 % lower than that of measured by the EC system.This could lead to a conclusion that not all the methane emission hot spots are recognized and accounted for in the analysis.However, when upscaling by the FW method the difference between the upscaled flux and the EC flux changes from underestimation to overestimation by ∼11 %, and can be associated with the methane emission model error, footprint model error, map accuracy or other unaccounted phenomena aside from the EC error.Constraining these uncertainties will provide further insight into major sources of error and will allow for more accurate validation of plotscale datasets.Introduction

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Full The detailed analysis of the EC footprint for upscaling methane fluxes in ecosystems with high spatial heterogeneity to ecosystem scale has been recently recognised as necessary (Sachs et al., 2010;Nicolini et al., 2013), but is still not being carried out in some studies (Sun et al., 2013).

Conclusions
Methane fluxes upscaled with the footprint-weighted average approach were shown to be of the same magnitude as the EC flux with a small overestimation (by 11.4 %).The temporal pattern of the EC flux on a daily time scale was captured, with r 2 = 0.7.
Most of the variations in the upscaled flux could be explained through a changing EC flux footprint and therefore a changing vegetation distribution within the footprint.This upscaling approach is more appropriate for model validation using EC measurements, especially for sites with a heterogeneous and irregular vegetation pattern, as it takes into account the vegetation composition in the EC tower footprint.Since the area-weighted upscaling lacks this dynamic spatial and temporal information, it is unsurprising that this approach failed to reproduce the temporal dynamics of the EC flux, which led to a low correlation with r 2 = 0.14, and an underestimation of the methane emissions by 27.4 %.The variations in fluxes from different classes could be largely explained by variations in soil temperature at depth of 4 cm and water table position (r 2 = 0.61).
The PEATLAND-VU model was able to capture not only the magnitude of the EC flux, but also the temporal dynamics on a daily time scale when the FW approach was used for upscaling.This makes it possible to use the model for the upscaling of methane fluxes to an ecosystem scale, delivering a better methane budget estimation, provided that a suitable vegetation map is available.Figures

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Full  Full GMRT 2.4 digital elevation model (DEM) was used to create the map (Ryan et al., 2009).Blue lines represent rivers (GSHHG 2.2.4 (Wessel and Smith, 1996) and Natural Earth 2.0.0 datasets). 24 wetlands are typically characterized by a heterogeneous pattern in both vegetation and hydrological conditions.This heterogeneity leads to a high spatial variation in methane (CH 4 ) fluxes in these ecosystems.The net exchange of CH 4 in northern wetlands between the soil and the atmosphere is a balance of production and consumption Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | acquired on 19 August 2010 was used to produce a high-resolution vegetation map.A maximum likelihood classification algorithm implemented in ENVI 4.8 (ITT Visual Information Solutions, Boulder, USA) was used for the classification.31 georeferenced plots with known vegetation and land cover type were used as training areas for 5 vegetation types.The resulting resolution of the map is 2 m.Because the model uses the classification by van Huissteden et al. ( Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | the summer periods from 2004 to 2010 excluding 2007 data from the floodplain.The model operates with classification defined by van Huissteden et al. (2005) and has daily temporal resolution.For a detailed description of the model we refer to Mi et al. (2013).
) where φ(x, y) is the 2-D footprint function, σ(x) = σ v ×x/u is the cross-wind dispersion, u is the effective plume velocity, σ v is the constant crosswind fluctuation, ξ the flux length scale, µ = (1 + m)/r is a constant, r = 2 + m − n the shape factor, m exponent of the wind velocity power law, n exponent of the eddy diffusivity power law.The model uses a gamma distribution for along-wind and a gaussian distribution for cross-wind footprint estimation.Because the model is based on the Monin-Obukhov Introduction Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | FW represents the ecosystem scale methane flux, φ(x, y) the footprint (or source area) weight value at point (x, y), F (x, y) the methane emission rate at point Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Figure 6 shows the methane fluxes from the PEATLAND-VU model for 2008 and 2009.The TD1 class shows near zero emissions throughout both periods.The TW1 and TW4 classes show little variation in methane emissions during both periods, which is partly Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Fig. 1 .
Fig. 1.Elevation map of Northeastern Siberia.Location of the validation site is marked with a white star.GMRT 2.4 digital elevation model (DEM) was used to create the map(Ryan et al., 2009).Blue lines represent rivers(GSHHG 2.2.4 (Wessel and Smith, 1996)  and Natural Earth 2.0.0 datasets).

Fig. 1 .
Fig. 1.Elevation map of Northeastern Siberia.Location of the validation site is marked with a white star.GMRT 2.4 digital elevation model (DEM) was used to create the map(Ryan et al., 2009).Blue lines represent rivers(GSHHG 2.2.4 Wessel and Smith, 1996, and Natural Earth 2.0.0 datasets).

Fig. 2 .Fig. 6 .F
Fig. 2. Temporal dynamics of daily soil temperature at 4 cm depth (solid line), water table position (dashed line) and daily precipitation (bars) for the EC measurement periods in 2008 and 2009.

Fig. 8 .
Fig. 8. Difference between the upscaled fluxes from the two methods vs. "vegetation composition difference".Colours from white to red show the magnitude of the EC flux.

Table 1 .
Vegetation classification and fractional area.
• C] and water table[cm]