difficulties of continuous field observation as well as complex physics of seasonal changes associated with freeze-thaw cycle. Indeed, observational data for the Arctic are much less abundant compared to the other continental regions. The worldwide FLUXNET network that integrates micrometeorological observations (http://fluxnet.fluxdata.org/) has over 700 sites located below 65 o N but fewer than 40 sites at higher latitudes (> 65 o N ) (Falge et al., 2016). A number of research teams have carried out studies to characterize surfaceenergy and water budgets in the Arctic, but direct measurements of water and energy fluxes are still sparse in space and time (Cristóbal et al., 2017). For example, Beringer et al., (2005) measured energy fluxes along a vegetation gradient and found an increase in growingseason latent heat and sensible heating along a tussock tundra - spruce forest ecotone in Alaska. Soegaard et al. (2001) reported two-year summer energy fluxes at Zackenberg (Greenland). A long-term record of energy fluxes focusing on summer seasons was reported later for the same site (Lund et al., 2014). Lund et al. (2017) later analyzed differences in energybudgets across tundra, snow, and ice surfaces at five sites in Greenland. Lloyd et al. (2001) studied surfaceenergy fluxes duringgrowing seasons at four sites across the European Arctic. Besides the scarcity of field data, the lack of energy budget closure remains to be a major issue in the analysis of energybudgets in the Arctic. Observed surfaceenergy imbalance at some sites can reach 20% (Soegaard et al., 2001; Lund et al., 2017). This level of energy imbalance was commonly attributed to instrumental and model uncertainties, inaccurate estimation of storage terms, and the lack of representativeness due to the small scale of heat flux observations (Wilson et al., 2002; Foken, 2008; Lund et al., 2014).
For the high latitudes of continental North America (Alaska and Canada), numerous studies have examined these greening trends, also using coarse-resolution, multi- spectral remote sensors. Goetz et al ( 2005 ) found increased photosynthetic activity (i.e. NDVI), using the 8 km AVHRR data for North American tundra over the period 1981–2003, including an earlier onset to the tundragrowingseason. Bunn et al ( 2005 ) analyzed the same dataset from 1981 to 2000, and found that tundra photosynthetic activity responded largely to maximum summer temperatures, which increased over this time period. For Canada specifically, Jia et al ( 2009 ) found that greening has occurred in all five Arctictundra bioclimate subzones (Walker et al 2005 subzones A–E ranging from north to south), with increases in peak NDVI of 0.49–0.79% yr −1 for the High Arctic (subzones A–C) and increases of 0.46–0.67% yr −1 for the Low Arctic (subzones D–E) over the period 1982–2006. Arctic subzones A–C exhibited a trend of earlier peak NDVI over time, whereas subzones D–E had earlier onsets of vegetation growth. In a finer resolution study, Pouliot et al ( 2009 ) used both 1 km AVHRR data and 30 m Landsat data to identify significant positive trends in NDVI for 22% of the Canadian land surface from 1985 to 2006, with some of the greatest increases occurring in the tundra. Olthof et al ( 2008 ) also indicated that increasing vegetation trends in Canadian tundra were greater for areas dominated by vascular plants, as opposed to those that were lichen-dominated.
The importance of snow in controlling functional trait com- position of tundra vegetation may rise from several mechanisms. Firstly, SCD limits the length of the growingseason, but it is also tightly linked to the thickness of the snowpack and consequently its insulating capacity. Thus, snow has strong local control on both summer and winter thermal conditions and incoming solar energy (32, 40, 41). Secondly, snow conditions are also directly or indirectly linked to many environmental factors other than en- ergy and temperatures. These include, for example, soil mois- ture, wind desiccation, ice crystal abrasion, soil forming processes, and nutrient mineralization (32, 41, 42). All these mechanisms may select plant species according to their life forms, size, structure, and biochemistry and thus act as strong local filters, producing a wide range of functionally different plant communities along the snow gradients. The fine-scale snow accumulation is largely controlled by local topography in tundra, but topography is also driving soil moisture and water flow, which are not linked to snow and meltwaters. However, we controlled the soil moisture effects emerging from local topog- raphy by including a topographic wetness index in our models. This enabled us to dissect the unique impacts of SCD on species traits, not blurred by the potentially confounding effect of the local topography.
4.3. Beaver dam increases in arctictundra regions
While we have not addressed the causes of increases in beaver dam building in this study, it could be linked to three key components of changes occurring in the Arctic. First, the increased growingseason length and increase in shrub cover and extent provide new forage and shelter necessary for beavers to survive in these landscapes (Tape et al 2006 , 2018 ). Second, changes in winter climate are dominating the annual climate change signal in this region (Stafford et al 2000 ). The increase in winter air temperatures combined with more winter snowfall in Arctic Alaska is likely increas- ing the over-wintering habitat potential for beavers in a number of different types of thermokarst water bodies and as a result of thinner seasonal winter ice growth (Arp et al 2018 ). The shorter winters also reduce the duration that beavers must remain in their lodges relying on caches from the previous summer. Third, the change in native subsistence and hunt- ing lifestyles that has occurred in this region and the reduction in the desire of beaver pelts and underfur for fashionable attire could be contributing to our
What were the controls on NEE across the burn severity gradient?
Diel environmental variability explained a majority of the half-hourly NEE variability, but played less of a role in determining NEE at longer timescales. Half-hourly NEE at each site was mostly controlled by incoming light, while EVI2 controlled weekly NEE across the burn severity gradient (Figs. 5 and 8). EVI2s ability to capture weekly NEE changes across the burn severity gradient without any other environmental information reﬂected the shift from abiotic to biotic control as NEE is temporally integrated (Richardson et al. 2007, Stoy et al. 2009a). EVI2 and NEE are functionally related to canopy leaf area and the photosynthetic potential of arctic plant canopies (Shaver et al. 2007, Lund et al. 2009). Burn severity inﬂuenced leaf area recovery in the ﬁrst postﬁre growingseason, which directly impacted carbon sequestration (Figs. 7 and 9). Recovery of tundra vegetation from ﬁre is dominated by regeneration of tussocks with little seed germination during the ﬁrst postﬁre growingseason (Wein and Bliss 1973). F IG . 10. Cumulative summer net ecosystem exchange differences (NEE D ) as a function of changes in the probability
Abstract: Vegetation changes, such as shrub encroachment and wetland expansion, have been observed in many Arctictundra regions. These changes feed back to permafrost and climate. Permafrost can be protected by soil shading through vegetation as it reduces the amount of solar energy available for thawing. Regional climate can be affected by a reduction in surface albedo as more energy is available for atmospheric and soil heating. Here, we compared the shortwave radiation budget of two common Arctictundra vegetation types dominated by dwarf shrubs (Betula nana) and wet sedges (Eriophorum angustifolium) in North-East Siberia. We measured time series of the shortwave and longwave radiation budget above the canopy and transmitted radiation below the canopy. Additionally, we quantified soil temperature and heat flux as well as active layer thickness. The mean growingseason albedo of dwarf shrubs was 0.15 ± 0.01, for sedges it was higher (0.17 ± 0.02). Dwarf shrub transmittance was 0.36 ± 0.07 on average, and sedge transmittance was 0.28 ± 0.08. The standing dead leaves contributed strongly to the soil shading of wet sedges. Despite a lower albedo and less soil shading, the soil below dwarf shrubs conducted less heat resulting in a 17 cm shallower active layer as compared to sedges. This result was supported by additional, spatially distributed measurements of both vegetation types. Clouds were a major influencing factor for albedo and transmittance, particularly in sedge vegetation. Cloud cover reduced the albedo by 0.01 in dwarf shrubs and by 0.03 in sedges, while transmittance was increased by 0.08 and 0.10 in dwarf shrubs and sedges, respectively. Our results suggest that the observed deeper active layer below wet sedges is not primarily a result of the summer canopy radiation budget. Soil properties, such as soil albedo, moisture, and thermal conductivity, may be more influential, at least in our comparison between dwarf shrub vegetation on relatively dry patches and sedge vegetation with higher soil moisture.
The image was corrected to the World Geodetic System (WGS) 84 ellipsoid and the Universal Transverse Mercator (UTM) Zone 6. Geometric correction was performed by using a Trimble GeoXT in the field, during the summers of 2006 and 2007, and differential correction using the Toolik Field site’s base station (http://www.uaf.edu/toolik/gis/TFS GIS gps base.html). RMSE was around 5 m (or one pixel) throughout the image. Ground control points (GCPs) away from the haul road and the pipeline (Fig. 1) were difficult to accurately place and increased the error rate, especially at points away from cultural features. All subsequent data were georectified to the image using 64 GCPs with RMS error of 4.9 m, which is less than a pixel (5 m) using a postprocessed differential correction. The image we selected contained some cloud cover, because no images could be located for the short growingseason that contained zero percent cloud cover. Therefore, due to the low sun angle, the clouds and their shadows obscured various portions of the scene. Clouds were confused with barren areas and shadows were similar to water; therefore both clouds and shadows were removed from the scene prior to analysis. The field work was conducted during the summers of 2006 and 2007, with approximately 10 field days each summer. More than 350 ground reference data points were created around Lake Toolik and in selected watersheds within the research area. The watersheds were chosen from the NSF grant specifications and to optimize helicopter cost. Most of the watersheds in the image were not easily accessible without a helicopter which in turn was extremely expensive. The costs of the helicopter time and expense of the length of stay at the field accommodations were
For Peer Review Supplementary Material 2. Methods in the systematic review 1. Data collection
This article is based on a literature review of chamber-derived CO 2 flux studies during the growingseason in the Arctictundra. The survey was conducted via ISI Web of Science (WoS) for articles published in the years 2000-2016. The search was carried out using a query that accounted for the region, scale, flux terminology, and different vegetation types: (tundra or arctic) and ecosystem and (“CO2 flux” or “carbon dioxide emissions” or “greenhouse gas exchange” or “CO2 exchange” or “carbon exchange” or “carbon flux”) and (meadow or sedge or tussock or hummock or heath or herb or grass or grassland or graminoid or forb or moss or bryophyte or lichen or "cushion plant" or shrub or tree). The query resulted in 242 articles out of which we included approximately 20 % of the studies. Firstly, we wanted to focus on soil chamber measurements and, therefore, excluded studies with eddy covariance or leaf cuvette measurements only. Secondly, only studies that included growingseason measurements were taken into account. Studies with GPP, ER, and/or NEE measurements were included. Boreal regions were excluded from the review as we wanted to focus on tundra patterns and processes only, although they are usually included in the carbon balance assessments of Arctic regions (Belshe et al., 2013; McGuire et al., 2010; Treat et al., 2015). However, we included studies with sub-Arctic and Arctic peatlands and fens due to their importance in tundra carbon cycling. Additional articles were derived from the references of the selected publications.
Abstract. The Arctic has become generally a warmer place over the past decades leading to earlier snow melt, permafrost degradation and changing plant communities. Increases in precipitation and local evaporation in the Arctic, known as the acceleration components of the hydrologic cycle, cou- pled with land cover changes, have resulted in significant changes in the regional surfaceenergy budget. Quantifying spatiotemporal trends in surfaceenergy flux partitioning is key to forecasting ecological responses to changing climate conditions in the Arctic. An extensive local evaluation of the Two-Source Energy Balance model (TSEB) – a remote- sensing-based model using thermal infrared retrievals of land surface temperature – was performed using tower measure- ments collected over different tundra types in Alaska in all sky conditions over the full growingseason from 2008 to 2012. Based on comparisons with flux tower observations, refinements in the original TSEB net radiation, soil heat flux and canopy transpiration parameterizations were identified for Arctictundra. In particular, a revised method for estimat- ing soil heat flux based on relationships with soil tempera- ture was developed, resulting in significantly improved per- formance. These refinements result in mean turbulent flux er- rors generally less than 50 W m −2 at half-hourly time steps, similar to errors typically reported in surfaceenergy bal- ance modeling studies conducted in more temperate climatic regimes. The MODIS leaf area index (LAI) remote sensing product proved to be useful for estimating energy fluxes in
snowmelt on the seasonal surfaceenergy budget is strongly connected to the storage of meltwater in the soil and its evap- oration and transpiration over the subsequent growing sea- son. A higher proportion of soil moisture, combined with a high atmospheric moisture demand, generally stimulates ET. In our study, the impact of the availability of soil moisture from snowmelt and its loss through ET on the surface en- ergy budget was most pronounced at the heath. Here we ob- served that low soil moisture content and lack of summer- time precipitation in 2013 amplified H at the cost of LE and G, while increased soil moisture in 2014 and a pronounced rainfall event in the middle of the growingseason favoured LE and G at the cost of H. Further, the Bowen ratio of the heath during the same year showed that energy partitioning into H and LE was similar to the wet fen. In contrast, the wet fen showed attenuated behaviour of LE, ET, H and G to the variability in snow meltwater as the fen receives its mois- ture supply mostly from minerotrophic water supply, which remained relatively stable over the 2 study years. Growingseason variability in the partitioning of the surfaceenergy balance components was also observed at a polygonal tundra site in Siberia (Boike et al., 2008). However, the observed differences were mainly driven by variability in summertime precipitation.
ii. The large-scale advection of warm air is usually re- lated to increased cloudiness, which alters the net ra- diation and thus the entire surfaceenergy balance. Dur- ing the spring period, our results indicate that the ob- served inter-annual differences in the ground tempera- tures are caused by different air temperatures, which are presumably related to the lager scale atmospheric ad- vection processes. During the summer months, the net radiation is reduced for cloud-covered skies, which in turn leads to surface cooling. According to our mea- surements, this mainly affects the magnitude of tur- bulent heat fluxes. While the surface temperature is lower under cloudy conditions, this only marginally de- creases the strong temperature gradient in the soil, so that the impact of clouds on the ground heat flux is mi- nor. Hence, the thawing dynamics of the active layer is only marginally affected by changed cloudiness. Dur- ing the fall period, the contrary effect of a cloud cover is observed, as clouds reduce the long-wave radiative losses, leading in turn to increased surface tempera- tures. The impact of clouds on the ground heat budget is observed to be largest during the fall season, when presumably increased cloudiness delays the refreezing process in 2007, while the average air temperatures are similar. This twofold influence of the cloud cover on the net radiation in the Arctic is confirmed in several stud- ies (Curry et al., 1996; Intrieri et al., 2002; Shupe and Intrieri, 2004).
NLCC pixel is consistent within the same class. During warm and dry periods, bare soil surfaces in the study area heat up the most with the average daily LST being about 3 to 8 ◦ C warmer than wet sedge surfaces. This difference could be expected due to large difference in available surface moisture. After snow melt, the bare soil surface dries out, and no moisture input is provided through precipitation. The surface resistance increases limiting the latent heat flux so that more energy is available for the sensible heat flux and thus for warming the surface (Seneviratne et al., 2010 ). Over both open water and mixed wetland surfaces, however, moisture supply at the sur- face is unlimited and the increased latent heat flux has a cooling ef- fect on the surface. Over wet sedge surfaces, the soil underneath the surface cover of mosses and grasse remains saturated throughout the thawed season. Wet sedge surfaces, however, do not show the same de- gree of cooling as mixed wetland surfaces. This is probably due to a limited latent heat flux from these sites. Evapotranspiration measure- ments over moss-dominated wetland sites in an Arctic coastal wetland near Barrow, Alaska, showed that an increased bulk surface resistance suppressed the evapotranspiration during large atmospheric demands even if soils were wet (Liljedahl et al., 2011 ). Transpiration from vas- cular plants is limited due to the relatively low cover of grasses. Veg- etation cover in wet sedge surfaces is dominated by mosses. Although the underlying soil remains saturated, the moss-dominated vegetation cover dries out during warm and dry periods which reduces latent heat fluxes (Oechel and Van Cleve, 1986 ; Muster et al., 2012 ). Although barren surfaces equally dry out during the warm period, they do not heat up as much as bare soil but remain about 5 ◦ C cooler. This is likely due to the difference in albedo. Barren surfaces exhibit an albedo that is about 7 % higher than the albedo of bare soil surfaces, so that more of the incoming shortwave radiation is reflected back into the atmo- sphere and less net radiation is available for the sensible and latent heat fluxes.
Results using CHIRPS and the GeoWRSI model showed that reductions in rainfall during critical growth stages could have led to increased water stress for maize in rainfed areas of central Tanzania and in northeast Tanzania during the long rains season. At a regional average the differences were not significant. However, the per-pixel trend maps showed that changes to maize yield potential were substantial in areas of Shinyanga, Arusha, Singida, Dodoma, Manyara, and in neighboring provinces. These are areas that receive 300mm to 700 mm annual rainfall. Shinyanga and Arusha are important contributors to national maize production (Rowhani et al., 2011), so yield declines in these areas could impact domestic food supply and the economy. No major changes to water stress were identified in high production areas of the southern region. Long term records for regional and local level yields would be helpful for identifying signs of increased crop water stress in the central and north-central region. Farmer access to agricultural inputs, pests and disease, and climate extremes are some of the many factors that influence yields. In terms of delayed onset of the growingseason, which was reported in Mary and Majule (2009) in Singida province and by Vrieling et al. (2013) for larger areas, we found that the average date of onset was later in the more recent period in central and northeast Tanzania (long rains). We also examined per- pixel trends in the onset date (not shown) and found delays of one to two months in Singida and Manyara. However, no changes in the onset date were statistically significant.
The aim of the presented paper was to map the course of inﬁ ltration during the growingseason of 2010 in a winter wheat stand on a selected locality in the Sazomín cadastral area on the basis of selected hydro-physical properties of soil (speciﬁ c weight, reduced volume weight, actual soil moisture, absorptivity, retention water capacity, porosity, capillary, semi-capillary and non-capillary pores and aeration) evaluated from the analyses of undisturbed soil samples. In order to assess the inﬁ ltration capacity of soil at the U Jasana locality in the season April–October, four surveys were realized always with three measurements within each of the surveys. The measurement of inﬁ ltration took place in the form of basin irrigation. To evaluate ﬁ eld measurements of inﬁ ltration empirical relations were used, namely Kostiakov equations. The highest cumulative inﬁ ltration and speed of inﬁ ltration were noted in June at the high actual soil moisture and closed stand. In case of October measurement, eﬀ ects of agro-technical operations became evident on the slightly lower inﬁ ltration capacity of soil as compared to June measurements at nearly identical moisture conditions. The lowest inﬁ ltration capacity of soil reaching the same level, namely in spite of diﬀ erent moisture conditions and the stand character (July – full-grown stand, August – stubble-ﬁ eld) was found in July and August.
During of the ﬁ rst year of measurement (2008), from April to November, has proceeded ﬁ eld mea- surement of soil inﬁ ltration ability at Žabčice locality. To get statistically conclusive results, measure- ment runs in three repetitions and data are subsequently averaged. Three sets of homocentric metal cylinders were used for the measurement. Measurement of inﬁ ltration has been preceded by an over- ﬂ ow. Empirical equations according to Kosťjak were used for evaluation of ﬁ eld measurement. At the same time there were ensured intact soil samples for laboratory determination of soil physi- cal properties using Kopecky cylinders at depths of 10, 20 and 30 cm, and for the calculation of se- lected hydro-physical parameters of soil. reduced volume weight, actual monture, porosity, aeration and other.
Slow bearing—Apples trees have some specific characteris- tics that influence their management in short-season, high- altitude climates. First of all, they are often slow to bear fruit. This can be an especially difficult problem in short-season cli- mates where the trees may take longer to reach bearing size. If given proper care, apple trees should begin to bear decent quantities of fruit in 4 to 6 years. Use of dwarfing rootstocks can result in earlier bearing. Trees on Bud 9 rootstock will bear fruit as early as the third year after planting. If apple trees are growing rapidly at the end of several years, but not bearing fruit, it can be an indication of too much nitrogen fer- tilizer, improper or excessive pruning, or poor training. When slow bearing occurs, try eliminating fertilizer applica- tions until the tree begins to bear. If the tree is in a lawn area, avoid fertilizing the grass around the tree. Another trick to encourage bud formation and fruit production is to bend the branches downward. Do this by pulling the major branches downward to form a 90 degree angle from the trunk and tie them (under tension) in place using ground stakes.
Abstract. In response to the increasing global demand for energy, oil exploration and development are expanding into frontier areas of the Arctic, where slow-growingtundra vegetation and the underlying permafrost soils are very sensitive to disturbance. The creation of vehicle trails on the tundra from seismic exploration for oil has accelerated in the past decade, and the cumulative impact represents a geographic footprint that covers a greater extent of Alaska’s North Slope tundra than all other direct human impacts combined. Seismic exploration for oil and gas was conducted on the coastal plain of the Arctic National Wildlife Refuge, Alaska, USA, in the winters of 1984 and 1985. This study documents recovery of vegetation and permafrost soils over a two-decade period after vehicle trafﬁc on snow-covered tundra. Paired permanent vegetation plots (disturbed vs. reference) were monitored six times from 1984 to 2002. Data were collected on percent vegetative cover by plant species and on soil and ground ice characteristics. We developed Bayesian hierarchical models, with temporally and spatially autocorrelated errors, to analyze the effects of vegetation type and initial disturbance levels on recovery patterns of the different plant growth forms as well as soil thaw depth. Plant community composition was altered on the trails by species-speciﬁc responses to initial disturbance and subsequent changes in substrate. Long-term changes included increased cover of graminoids and decreased cover of evergreen shrubs and mosses. Trails with low levels of initial disturbance usually improved well over time, whereas those with medium to high levels of initial disturbance recovered slowly. Trails on ice-poor, gravel substrates of riparian areas recovered better than those on ice-rich loamy soils of the uplands, even after severe initial damage. Recovery to pre-disturbance communities was not possible where trail subsidence occurred due to thawing of ground ice. Previous studies of disturbance from winter seismic vehicles in the Arctic predicted short-term and mostly aesthetic impacts, but we found that severe impacts to tundra vegetation persisted for two decades after disturbance under some conditions. We recommend management approaches that should be used to prevent persistent tundra damage.
Experiments were carried out in permanent grassland (PG) and arable land with stand Triticale grenado. Following the harvest of the previous crop (Biscay wheat), which took place at the end of August 2010, stubble under-ploughing was carried out followed by ploughing in mid-September. Immediately a er the pre-sowing soil preparation using a compactor on October 1, the sowing of triticale was carried out followed by the soil surface compaction and spraying herbicides. In spring 2011, a regeneration fertilization was carried out using ammonium nitrate with limestone (March 15), followed by production fertilization using ammonium nitrate with urea (April 28). A fungicide spray was applied on May 11. Harvesting took place on August 21, followed by stubble under-ploughing, manure spreading and ploughing on September 25. The PG was fertilized using ammonium sulphate (April 6) and liquid manure, in two doses (April 13, June 18). Mowing of the growth took place on May 18, June 14 and August 31, followed by harvesting hay on the following day.
Subzone 1: cushion-forb subzone. In the coldest portions of the Arctic, the major parts of the land surface are largely barren, often with <5% cover of vascular plants. Permanent ice covers large areas of the land. Woody plants are absent. Lichens, bryophytes, cyanobacteria, and scattered forbs (e.g. Papaver, Draba, Saxifraga, Stellaria) are the dominant plants of the sparse vegetation cover. Many of the forbs, lichens and mosses have a compact cushion growth form. In midsummer, the Arctic poppy, Papaver radicatum s.l., is the most conspicuous plant over large portions of this subzone. Soil lichens, mosses, and liverworts can cover a high percentage of the surface, particularly in more maritime areas such as Novaya Zemlya (Alexandrova 1980). Rushes (Luzula and Juncus) and grasses (Alopecurus, Puccinellia, Phippsia, and Dupontia) are the main graminoid groups. Sedges are rare and wetlands lack organic peat layers. Well- vegetated surfaces occasionally occur on mesic sites, but there is little contrast in the composition of vegetation on mesic sites, streamside sites, and snowbeds The vascular-plant ¯ora is extremely depauperate, consisting of only about 50±60 species (Young 1971). On ®ne-grained soils, the extremely cold temperatures and the thin sparse plant canopy induce intense frost activity, which forms networks of small (<50 cm dia- meter) nonsorted polygons, and plants are con®ned mainly to the depressions between the polygons (Cher- nov & Matveyeva 1997).
However, using a non-footprint weighted method to upscale fluxes is not always successful, as shown by Budischev et al. , who achieved a low correlation (0.14) between upscaled and EC fluxes, improving these results with the inclusion of a footprint analysis. Parmentier et al.  found good agreement between upscaled and EC tower emissions at the same site, indicating that temporal variations (both within and between years) will have a strong influence on the upscaled results. Our results support the use of a footprint-weighted upscaling method to more accurately upscale the chamber flux measurements. However, there was not a 1:1 relationship between upscaled fluxes using the footprint model and the EC towers, indicating that this methodology may underestimate the overall flux, the opposite to Budischev et al. , who showed the footprint model method produced a slight overestimation of the flux in comparison to the EC towers, attributed to localised differences in environmental conditions across the measurement period and vegetation classification error. Maruschak et al.  also upscaled chamber measurements aross a sub-arctictundra ecosystem, using a high-resolution vegetation map and weighting the fluxes by their relative area. They found that this method again indicated an overestimation of the chamber measurements in comparison to the EC towers. They noted that the plots chosen for chamber measurements had a higher leaf area index (LAI) in comparison to similar areas in the region. By correcting for the higher LAI in the measurement plots, they found a tighter relationship between upscaled chamber measurements and the EC towers. To improve upscaling estimates, understanding the phenological dynamics of the vegetation communities present could be useful, rather than just the spatial variation at one point in time [91,92]. It is interesting to see how much of an impact ‘drier’ sedge communities can have on the flux (between 10% and 38% of the measured flux) , further highlighting how different vegetation communities may contribute to CH 4 budgets and their variability [30,91]. The use of automatic chambers could