Surface water inundation in the boreal-Arctic: potential impacts on regional methane emissions

Northern wetlands may be vulnerable to increased carbon losses from methane (CH4), a potent greenhouse gas, under current warming trends. However, the dynamic nature of open water inundation and wetting/drying patterns may constrain regional emissions, offsetting the potential magnitude of methane release. Here we conduct a satellite data driven model investigation of the combined effects of surface warming and moisture variability on high northern latitude (⩾45° N) wetland CH4 emissions, by considering (1) sub-grid scale changes in fractional water inundation (Fw) at 15 day, monthly and annual intervals using 25 km resolution satellite microwave retrievals, and (2) the impact of recent (2003–11) wetting/drying on northern CH4 emissions. The model simulations indicate mean summer contributions of 53 Tg CH4 yr−1 from boreal-Arctic wetlands. Approximately 10% and 16% of the emissions originate from open water and landscapes with emergent vegetation, as determined from respective 15 day Fw means or maximums, and significant increases in regional CH4 efflux were observed when incorporating satellite observed inundated land fractions into the model simulations at monthly or annual time scales. The satellite Fw record reveals widespread wetting across the Arctic continuous permafrost zone, contrasting with surface drying in boreal Canada, Alaska and western Eurasia. Arctic wetting and summer warming increased wetland emissions by 0.56 Tg CH4 yr−1 compared to the 2003–11 mean, but this was mainly offset by decreasing emissions (−0.38 Tg CH4 yr−1) in sub-Arctic areas experiencing surface drying or cooling. These findings underscore the importance of monitoring changes in surface moisture and temperature when assessing the vulnerability of boreal-Arctic wetlands to enhanced greenhouse gas emissions under a shifting climate.


Introduction
Wetlands and lakes cover approximately 2-8% of the boreal-Arctic region (Watts et al 2012), with large fluctuations in surface water extent resulting from seasonal melt cycles, summer precipitation and drought events (Schroeder et al 2010, Bartsch et al 2012, Helbig et al 2013. Wet surface conditions and characteristically colder temperatures greatly reduce the rate of organic carbon decomposition in northern wetland environments (Harden et al 2012, Elberling et al 2013. As a result, over 50% of the global soil organic carbon pool is stored in these regions (Turetsky et al 2007, Hugelius et al 2013. Landscapes with inundated or moist surfaces are particularly vulnerable to carbon loss as methane (CH 4 ) (Turetsky et al 2008, Fisher et  Various wetland maps have been used to define the extent of methane emitting area (Matthews and Fung 1987, Aselmann and Crutzen 1989, Reeburgh et al 1998, Lehner and Döll 2004, Schneider et al 2009, Glagolev et al 2011, but their static nature fails to capture dynamic spatiotemporal variations in surface wetness within boreal-Arctic environments. As a result, modeling studies are increasingly using satellite based inundation data to characterize the impact of changing surface water coverage on regional methane emissions (Petrescu et al 2010, Riley et al 2011, Zhu et al 2011, Meng et al 2012, Bohn et al 2013, Wania et al 2013. These datasets include the GIEMS (Global Inundation Extent from Multi-Satellites) record (Prigent et al 2007, Papa et al 2010 that estimates monthly inundation within 0.25°resolution grid cells using microwave observations from the Special Sensor Microwave/Imager (SSM/I) and the ERS scatterometer. However, the GIEMS record only spans from 1993 to 2007 and relies on visible (0.58-0.68 μm) and near-infrared (0.73-1.1 μm) Advanced Very High Resolution Radiometer (AVHRR) data to account for vegetation canopy effects on microwave retrievals (Papa et al 2010). An alternative method, described by Schroeder et al (2010) and integrated into methane studies for western Siberia (Bohn et al 2013, Wania et al 2013, avoids the use of optical/ infrared sensor information by incorporating QuikSCAT scatterometer and 6.9 GHz passive microwave data from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to determine 25 km grid fractional water coverage at 10 day intervals.
A recent approach introduced by Jones et al (2010) uses AMSR-E 18.7 and 23.8 GHz, H-and V-polarized brightness temperatures to retrieve 25 km resolution daily fractional open water (Fw) inundation, and does not require ancillary information (e.g. AVHRR optical or QuikSCAT radar) to account for microwave scattering effects from intervening atmosphere and vegetation. The Jones et al (2010) AMSR-E Fw data have been used to evaluate recent seasonal and interannual inundation variability across the northern high latitudes and permafrost regions, with a demonstrated sensitivity to changes in the surface water balance, and a relatively low observation spatial uncertainty of approximately 4% (Watts et al 2012). The higher frequency 18.7 and 23.8 GHz brightness temperatures used to derive the AMSR-E Fw retrievals also minimize signal sensitivity to underlying soil moisture conditions (Jones et al 2010, Watts et al 2012). Although satellite optical and radar remote sensing can characterize wetland and open water distributions at finer (⩽150 m resolution) scales (Bartsch et al 2012, Rover et al 2012, Bohn et al 2013, Muster et al 2013, this information is often constrained to localized analyses with minimal repeat observations and is not yet conducive for the pan-Arctic wide monitoring of surface inundation. This study examines the potential implications of recent (2003-11) variability in surface wetness on methane efflux from northern high latitude (⩾45°N) wetlands, and the contrasting influence of regional changes in moisture and temperature on summer (May through September) emission budgets using satellite remote sensing and reanalysis information. We postulate that seasonal and inter-annual fluctuations in surface inundation can greatly limit the magnitude of methane release from wetland environments, particularly if summer warming coincides with periods of drought. Conversely, northern wetlands may be more susceptible to methane emissions when the extent and duration of surface wetness is sustained or increasing. We conducted a series of carbon and climate sensitivity simulations using the Joint UK Land Environment Simulator (JULES) methane emissions model (Clark et al 2011, Bartsch et al 2012, with input Fw means and maximums at 15 day, monthly, and annual intervals as derived from an AMSR-E global daily land parameter record (Jones et al 2010, Jones andKimball 2011a). In this study, Fw is defined as the proportional surface water cover within 25 km equal area AMSR-E grid cells (Watts et al 2012), and includes inundated soils, open water (e.g. lake bodies) and landscapes with emergent vegetation. We then evaluated the impact of recent temperature variability and wetting/drying on methane emission budgets for the northern wetland regions.

Study region
The land area considered in this analysis was determined using boreal-Arctic peatland maps (i.e. Gunnarsson and Löfroth 2009, Yu et al 2010, Franzén et al 2012, and the Regional Carbon Cycle and Assessment Processes (REC-CAP) tundra domain (McGuire et al 2012). To coincide with the spatial extent of AMSR-E Fw coverage, we removed grid cells having ⩾50% permanent ice or open water cover using the UMD Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (described in Jones et al 2010), and applied a conservative 25 km coastline buffer to minimize Fw retrieval contamination by ocean water. The resulting study region spans approximately 1.6 × 10 7 km 2 (figure 1), and contains 70% of northern continuous and discontinuous permafrost affected landscapes (Brown et al 1998).

Model description and calibration
The JULES model approach (Clark et al 2011, Bartsch et al 2012 accounts for the major factors (i.e. temperature, carbon substrate availability, landscape wetness) that control global methane emissions (Bloom et al 2010, Olefeldt et al 2013. Albeit relatively simple and lacking in detailed physical processes, this method is useful for pan-Arctic simulations because it avoids extensive parameterization requirements that can substantially increase estimate uncertainty (Riley et al 2011). The model regulates methane emissions according to available carbon substrate (C, kg m −2 ) and an efflux rate constant (k CH 4 , d −1 ) that is modified by a temperature dependent Q 10 factor (Gedney et al 2004, Clark et al 2011. The temperature effects on methane production are controlled using daily input Modern Era Retrospectiveanalysis for Research and Applications (MERRA) surface soil temperature (T s , in kelvin) and a thermal reference state (T 0 , 273.15 K): For this analysis, we limit our investigation to non-frozen surface conditions defined using daily satellite passive microwave sensor derived binary (0 or 1) freeze/thaw (FThw) constraints (Kim et al 2013). The resulting daily fluxes (F CH 4 , CH 4 m −2 ) were averaged over a 15 day time step and scaled to the 25 km grid cell domain (tonne CH 4 cell −1 ) using Fw and volumetric soil moisture (θ, m 3 /m 3 ) information to regulate landscape methane emissions. Methane efflux from inundated portions of the grid-cell were assumed to be non-inhibited, whereas non-inundated cell fractions − Fw (1 )were weighted by θ to account for reduced methane loss due to oxidation. In this study, the emissions weighting process was applied at a 15 day time step to address potential delays in methanogen response following surface wetting or drying (Blodau andMoore 2003, Turetsky et al 2014).
The daily input T s and θ (⩽10 cm soil depth) records were obtained from the NASA GEOS-5 MERRA Land reanalysis archive with native 0.5°× 0.6°resolution  and posted to a 25 km resolution polar equal-area scalable earth grid consistent with the AMSR-E Fw data. The MERRA land parameters have been evaluated for high latitude regions, with favorable correspondence in relation to independent satellite microwave and in situ observations (Yi et al 2011, Watts et al 2014. Soil metabolic carbon (C met ) pools obtained from a Terrestrial Carbon Flux (TCF) model (Kimball et al 2009, Yi et al 2013 were used as the substrate for methanogenesis. The TCF carbon estimates reflect daily changes in labile plant residues and root exudates, and have been evaluated against existing soil organic carbon inventory records for the high latitude regions (described in Yi et al 2013). The C met inputs (kg C m −2 d −1 ) were generated for the study region by a 1000 yr spin-up of the model using a 10 yr (2000-09) record of MODIS 1 km resolution Normalized Difference Vegetation Index, MERRA daily surface meteorology and soil moisture inputs.
The JULES model k CH 4 and Q 10 parameters were calibrated using mean monthly eddy covariance methane fluxes (mg CH 4 m −1 d −1 ) from five northern wetland tower sites , in conjunction with mean MERRA reanalysis C met and T s climatology over the 2003-11 summer (May through September) period. A resulting Q 10 value of 3.7 and a k CH 4 rate of 3.7 × 10 −5 d −1 minimized the root mean square error (RMSE) differences between the model and flux tower observations at 17.62 mg CH 4 m −2 d −1 . A Q 10 of 3.7 was also used by Clark et al (2011) and is similar to those reported in other studies (Ringeval et al 2010, Waldrop et al 2010, Lupascu et al 2012. Further model verification was also obtained by evaluating summer flux chamber measurements (see supplementary table S1) from tundra (n = 15 site records), boreal wetland (n = 11) and lake (n = 17) locations.

Regional simulations
Grid-scale (25 km) wetland methane emissions were obtained using dynamic 15 day, monthly and annual summer AMSR-E Fw means or maximums from 2003 to 2011, to examine the potential impact of temporal Fw scaling on methane emission estimates. Methane simulations were also examined using a static mean summer Fw map derived from the 2003-11 record. The regional simulations were evaluated against NOAA ESRL atmospheric methane flask measurements (Dlugokencky et al 2013) from Barrow, AK, Lac LaBiche, CN, and Pallas Sammaltunturi, FI, to assess the ability of the model to capture between-year changes in methane concentrations that may correspond with fluctuations in wetland methane emissions (Lelieveld et al 1998). For Barrow and Sammaltunturi, the dry air mole fractions were available from 2003 through 2011; the Lac LaBiche data were available from 2008 onward. A Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT; Draxler andRolph 2013, Rolph 2013) model, with a 100 m receptor point altitude and input GDAS-1 meteorology (Rodell et al 2004), was used to obtain backward (30 day) atmospheric trajectories for each flask site, and showed the dominant source contributions at Barrow to originate primarily from northern Alaska, the Yukon River basin eastward to the Northwest Territories, and eastern Siberia. For the respective Lac LaBiche and Sammaltunturi locations, the major source regions were from northern Canada, or extending from Scandinavia eastward into western Russia. To determine the relative correspondence between modeled annual methane emission contributions and observed mean summer dry air mole fractions, Pearson product-moment correlation coefficients (r) were derived using spatial means from a 3 × 3 grid cell window centered on each flask location. Regional point correlation maps (Ding and Wang 2005) were also obtained by evaluating r (e j , a k ) for each grid cell within the methane source regions, where e j is the modeled mean summer emissions time series at a given cell location and a k is the atmospheric methane concentration time series at a flask sampling site.
Regional changes in surface water coverage, soil moisture and temperature were evaluated using a nonparametric Mann-Kendall trend analysis that accounts for serial correlation prior to determining trend significance (Yue et al 2002, Watts et al 2012. The Kendall rank correlations were applied to the mean summer AMSR-E Fw, and MERRA T s and θ records on a per-grid cell basis from 2003 to 2011. Trend significance was determined at a minimum 95% (p < 0.05) probability level. The Kendall trend was also applied to the modeled cumulative annual methane emissions to identify regions that may be vulnerable to increasing anaerobic carbon losses. A linear regression analysis was then used to determine the rate of change in the annual emission estimates.

Model evaluation against in situ methane flux observations
The model simulations captured overall temporal variability (r 2 = 0.65, p < 0.05) observed in the monthly tower eddy covariance records, with a RMSE value of 17.6 mg CH 4 m −2 d −1 that is similar to other regional studies (Meng et al 2012, Zhu et al 2013). Significant differences (α = 0.05; two-sample t-test with unequal variance) were not observed (figure S1) between the model estimates and mean monthly tower eddy covariance (t = 1.45, p = 0.15), boreal chamber (t = 0.05, p = 0.96), and northern lake (t = 0.79, p = 0.45) fluxes. However, the modeled fluxes were significantly smaller (t = 3.67, p < 0.01) than the tundra chamber observations and did not adequately capture larger (>140 mg CH 4 m −2 d −1 ) eddy covariance fluxes from a peatland site in northern Sweden 3.2. Regulatory effects of surface water and temperature on regional methane emissions 3.2.1. Wetland inundation characteristics. Approximately 5% (8.4 × 10 5 km 2 ± 6% SE) of the boreal-Arctic domain was inundated with surface water during the non-frozen summer season, as indicated by the 2003-11 AMSR-E Fw retrieval means. Over 63% of the wetlands were located in North America, primarily within the Canadian Shield region, and the majority of inundation occurred above 59°N within major wetland complexes, including the Ob-Yenisei and Kolyma Lowlands in Siberia (figure 2). A strong seasonal pattern in surface water was observed across the high latitudes, with an abrupt increase in May or early June following surface ice and snow melt, and the onset of spring precipitation (figure 3). In Eurasia, peak inundation occurred in June, followed by a gradual decline with summer drought and increased evaporative demand (Rawlins et al 2009, Schroeder et al 2010, Bartsch et al 2012, Watts et al 2012. In North America, the seasonal expansion of surface water continued through July, before beginning to subside with the onset of surface freezing.
The influence of wet/dry cycles on surface water extent was evident throughout the boreal-Arctic region. The summer of 2004 was the driest observed over the AMSR-E Fw record, with a 6% decrease in inundation from the long-term mean 3.2.2. Regional summer methane simulations. Summer methane emissions estimated for non-inundated land fractions averaged 47.8 ± 1.8 Tg CH 4 yr −1 over the northern wetlands. This increased to 53.2 ± 1.9 Tg CH 4 yr −1 when also considering contributions from inundated landscapes based on the 15 day AMSR-E Fw means. These results are within the range of emissions (39 to 89 Tg CH 4 yr −1 ) reported from previous modeling studies using other satellite-based Fw retrievals (table 1;     Wetland methane (CH 4 ) emissions and associated surface inundation extent determined by regional modeling studies using satellite microwave based surface water (Fw) retrievals to define the spatial extent of methane producing area. The Fw inputs include those scaled using 15 day, monthly and annual Fw means and maximums, or a static multi-summer Fw mean climatology. The methane emissions determined in this study are reported for inundated and combined inundated/non-inundated wetland landscape fractions. inversion analyses are also prone to uncertainties from atmospheric transport conditions and the limited number of observation sites within high latitude regions (Berchet et al 2013, Nisbet et al 2014. In northern wetlands, 80-98% of annual methane emissions occur during the summer (Alm et al 1999, Jackowicz-Korczyński et al 2010, Song et al 2012 due to strong thermal controls on methane production, carbon substrate and water availability . The influence of summer warming on regional methane emissions was apparent in the model simulations, with peak efflux occurring in June and July ( figure S2). This seasonal pattern has been observed in atmospheric methane mixing ratios across the Arctic (Aalto et al 2007, Pickett-Heaps et al 2011. Also evident was the impact of wet/dry cycles on regional methane contributions, with annual summer emission budgets fluctuating by ±4%, relative to the 2003-11 mean. The modeled emissions were lowest in 2004 despite anomalously high temperatures throughout the boreal-Arctic region (Chapin et al 2005), due to drought conditions in Alaska and northern Canada. In contrast, higher emissions in 2005 resulted from warm and wet weather in North America.
Surface moisture variability also influenced the correspondence between modeled emissions and summer atmosphere methane concentrations from the regional flask measurements. Regions showing a positive correspondence between modeled methane emissions and atmosphere concentrations largely reflected transport trajectories indicated in the HYSPLIT simulations (figure S3), with stronger agreement (r > 0.7, p ⩽ 0.05) occurring in areas characterized as open water or prone to periodic inundation (figure 4). Immediate to the flask sites, mean summer inundation varied from 2 to 10%, with moist soil fractions accounting for >85% of simulated emissions. At Lac LaBiche, annual emissions variability corresponding to wet soil fractions agreed well (r = 0.96, p = 0.02) with the flask observations. In contrast, relatively poor agreement was observed at Barrow and Sammaltunturi where emission patterns for inundated portions of the landscape corresponded more closely with atmospheric methane concentrations (table 2). At Barrow, the correspondence was similar (r > 0.43, p ⩽ 0.12) for model simulations using dynamic 15 day or annual Fw inputs, reflecting methane source contributions from thermokarst lakes and inundated tundra in the surrounding landscape (Dlugokencky et al 1995). In contrast, the modeled emissions at Sammaltunturi corresponded closely (r = 0.86, p < 0.01) with flask observations when accounting for 15 day variability in Fw extent, but showed minimal agreement when using annual Fw inputs. This discrepancy may be attributed to less open water cover in the surrounding region and a tendency for summer precipitation events to produce intermittent flooding due to shallow soil layers and limited drainage (Aalto et al 2007). These results differ from the Lac LaBiche site, where nearby peatlands are characterized by deeper layers of surface litter and moss (Dlugokencky et al 2011) that can substantially reduce surface water coverage.

Fw temporal scaling effects on summer methane budgets
Wetland studies have increasingly used satellite microwave remote sensing to quantify the extent of methane emitting area, given the strong microwave sensitivity to surface moisture and relative insensitivity to solar illumination constraints and atmospheric signal attenuation. Regional inundation information has been incorporated into model simulations using monthly, annual, or static multi-year Fw means ( (table 1). In this analysis, inundation extent within the boreal-Arctic wetlands increased by 6-15% and 13-31% when using respective mean monthly or annual AMSR-E Fw inputs instead of finer (15 day) temporal intervals. The coarser Fw temporal inputs resulted in respective increases in estimated methane emission budgets by 6% (t = 3.5, p < 0.01) and 17% (t = 8.7, p < 0.01) in Eurasia, relative to simulations using finer 15 day Fw temporal inputs. The impacts of Fw temporal scaling in North America were not significant (t ⩽ 0.7, p > 0.5), with corresponding increases of 0.7% (Fw monthly) and 2% (Fw annual) in estimated annual methane emissions. The observed emissions sensitivity to Fw scaling in Eurasia primarily results from precipitation and flooding events in early summer, followed by mid-summer drying (Serreze and Etringer 2003). As a result, Fw means considered over longer time intervals in these regions may be biased towards spring inundation conditions, and may not reflect regional decreases in surface wetness occurring during the warmer mid-summer months. Directly incorporating Fw maximums, sometimes used to quantify multi-year surface hydrology trends (Bartsch et al 2012, Watts et al 2012, also led to substantial increases (t >9.66, p < 0.01) in estimated methane emissions by 23-38% in North America and 21-54% in Eurasia for 15 day to annual time intervals relative to simulations using static Fw means.
3.4. Potential impact of regional wetting and drying trends on methane emission budgets Significant (p < 0.05) increases in surface inundation were observed over 4% (7.1 × 10 5 km 2 ) of the high latitude wetlands domain from 2003 to 2011, with substantial Fw wetting occurring within northern tundra and permafrost affected landscapes (figure 5). The extent of wetting increased to 6% (9.7 × 10 5 km 2 ) when including regions with slightly weaker trends (p < 0.1). While the regional wetting patterns may correspond with shifts in northward atmospheric moisture transport (Rawlins et al 2009, Skific et al 2009, Dorigo et al 2012, Screen 2013, trends within the Arctic Rim may be more closely influenced by thermokarst expansion, reductions in seasonal ice cover (Smith et al 2005, Rowland et al 2010, Watts et al 2012, and summer warming (figure 6(a)). In portions of western Siberia, localized cooling and residual winter snow melt (Cohen et al 2012) may also contribute to surface wetting. Regional drying was also observed across 3% (5.3 × 10 5 km 2 , p < 0.05) and 5% (7.6 × 10 5 km 2 , p < 0.1) of the northern wetland domain, particularly in northern boreal Alaska, eastern Canada and Siberia (figure 5). These declines in surface water extent may result from an increase in summer evaporative demand (Arp et al 2011)  The combined influence of warming and wetting in the AMSR-E Fw and reanalysis surface meteorology records contributed to an increase in methane emissions across 6% (p < 0.05, figure 6(b)) to 21% (p < 0.1) of the boreal-Arctic domain. This finding is similar to a projected 15% increase in methane emitting area with continued climate change in the northern wetland regions (Gao et al 2013). The corresponding mean rates of methane increase from 2003 to 2011 were 0.07 and 0.11 ± 0.02 Tg CH 4 yr −1 , respectfully, and occurred primarily in Canada and eastern Siberia where summer warming has been observed in both in situ measurements and reanalysis records (figure S4, Screen, Simmonds (2010), Smith et al 2010, Walsh et al 2011. A decrease in modeled methane emissions, associated with surface drying and cooling patterns, was also observed across 7% (p < 0.05; −0.12 ± 0.03 Tg CH 4 yr −1 ) and 15% (p < 0.1; −0.15 ± Tg CH 4 yr −1 ) of the study area, and offset regional gains in the methane emissions. When including all regions showing significant change (|t| > 3.6; p < 0.05), as indicated by the linear regression analysis (without grid cell screening using the more conservative Mann-Kendall tau), the respective rates of increase and decrease in boreal-Arctic wetland emissions were 0.56 ± 0.16 Tg CH 4 yr −1 and −0.38 ± 0.07 Tg CH 4 yr 1 .

Conclusions
Northern boreal-Arctic ecosystems may be especially vulnerable to methane emissions given climate warming, abundant soil carbon stocks, and a predominately wet landscape (Isaksen et al 2011, van Huissteden et al 2011, Olefeldt et al 2013. We found that 5% of northern wetlands were characterized by open water or emergent vegetation, with the majority of inundation occurring in the Canadian Shield lowlands and Ob-Yenisei drainage basins. Areas of significant (p < 0.05) increase in surface water extent were more prevalent within the Arctic Rim and may coincide with heightened summer precipitation (Landerer et al 2010, Screen 2013) or high latitude permafrost thaw (Rowland et al 2010, Watts et al 2012. The combined effect of surface wetting and warming contributed to regional increases of 0.56 Tg CH 4 yr −1 in estimated methane emissions, relative to the 2003-11 mean. Our analysis also revealed surface drying throughout the boreal zones of southern Sweden, western Russia and eastern Canada, as has been anticipated with increasing summer temperatures and drought conditions in the sub-Arctic (Frolking et al 2006, Tarnocai 2006. This landscape drying contributed to a 0.38 Tg CH 4 yr −1 decrease in summer emissions, and largely offset any increases in region-wide methane release. Regional modeling studies should consider the potential impacts of Fw scaling when prescribing the extent of methane emitting area in northern wetland regions, given the dynamic nature of surface water in northern landscapes (Schroeder Table 2. Mean summer fractional water (Fw) inundation and Pearson correspondence (r, with associated significance) between flask station dry air mole fractions (nmol CH 4 mol −1 ) and cumulative methane emission estimates (tonne CH 4 grid cell −1 ) within a 3 × 3 window centered at Barrow (BRW), Lac LaBiche (LLB) and Pallas Sammaltunturi (PAL). The model simulations incorporate dynamic 15 day or mean annual Fw; non-inundated grid cell fractions are regulated by surface soil moisture content (θ).

Dynamic Fw
Annual et al 2010, Bartsch et al 2012, Watts et al 2012. Our model sensitivity analysis shows significant differences in estimated annual emissions determined from coarse monthly or annual Fw relative to finer scale (15 day) inundation inputs. Although the estimated emissions rate of 53 Tg CH 4 yr −1 is similar to the results from previous studies, it may overestimate the magnitude of methane release from pan-boreal and Arctic wetland regions, given difficulties accounting for finer scale soil temperature and moisture heterogeneity , Parmentier et al 2011, Muster et al 2013 using coarse ⩾0.5°reanalysis information. The NASA Soil Moisture Active Passive (SMAP) mission (Entekhabi et al 2010) is scheduled to launch in late-2014 and will provide new global satellite L-band active and passive microwave observations of the land surface, with regular monitoring of northern soil thermal and moisture dynamics at 1-2 day intervals and moderate (3-9 km) spatial scales. These new observations may provide for the improved quantification of regional patterns and temporal dynamics in surface environmental conditions, which is needed to reduce uncertainty in regional and global methane emissions.