The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

Earth Syst Sci Data 13 5127ndash5149 2021httpsdoiorg105194essd-13-5127-2021copy Author(s) 2021 This work is distributed underthe Creative Commons Attribution 40 License

The BorealndashArctic Wetland and Lake Dataset (BAWLD)

David Olefeldt1 Mikael Hovemyr2 McKenzie A Kuhn1 David Bastviken3 Theodore J Bohn4John Connolly5 Patrick Crill6 Eugeacutenie S Euskirchen78 Sarah A Finkelstein9 Heacutelegravene Genet8Guido Grosse1011 Lorna I Harris1 Liam Heffernan12 Manuel Helbig13 Gustaf Hugelius214

Ryan Hutchins15 Sari Juutinen16 Mark J Lara1718 Avni Malhotra19 Kristen Manies20A David McGuire8 Susan M Natali21 Jonathan A OrsquoDonnell22 Frans-Jan W Parmentier2324Aleksi Raumlsaumlnen25 Christina Schaumldel26 Oliver Sonnentag27 Maria Strack28 Suzanne E Tank29

Claire Treat10 Ruth K Varner230 Tarmo Virtanen25 Rebecca K Warren31 and Jennifer D Watts21

1Department of Renewable Resources University of Alberta Edmonton AB T6G 2G7 Canada2Department of Physical Geography Stockholm University 10691 Stockholm Sweden

3Department of Thematic Studies ndash Environmental Change Linkoumlping University 58183 Linkoumlping Sweden4WattIQ 400 Oyster Point Blvd Suite 414 South San Francisco CA 94080 USA

5Department of Geography School of Natural Sciences Trinity College Dublin Dublin 2 Ireland6Department of Geological Sciences Stockholm University 10691 Stockholm Sweden

7Department of Biology and Wildlife University of Alaska Fairbanks Fairbanks AK 99775 USA8Institute of Arctic Biology University of Alaska Fairbanks Fairbanks AK 99775 USA9Department of Earth Sciences University of Toronto Toronto ON M5S 3B1 Canada

10Permafrost Research Section Helmholtz Centre for Polar and Marine ResearchAlfred Wegener Institute 14473 Potsdam Germany

11Institute of Geosciences University of Potsdam 14476 Potsdam Germany12Department of Ecology and Genetics Uppsala University 752 36 Uppsala Sweden

13Department of Physics and Atmospheric Science Dalhousie University Halifax NS B3H 4R2 Canada14Bolin Centre for Climate Research Stockholm University 10691 Stockholm Sweden

15Department of Earth and Environmental Sciences University of Waterloo Waterloo ON N2L 3G1 Canada16Ecosystems and Environment Research Program University of Helsinki 00014 Helsinki Finland

17Department of Plant Biology University of Illinois Urbana IL 61801 USA18Department of Geography University of Illinois Urbana IL 61801 USA

19Department of Earth System Science Stanford University Stanford CA 94305 USA20US Geological Survey Menlo Park CA USA

21Woodwell Climate Research Center Falmouth MA 02540 USA22Arctic Network National Park Service Anchorage AK 99501 USA

23Centre for Biogeochemistry in the AnthropoceneDepartment of Geosciences University of Oslo 0315 Oslo Norway

24Department of Physical Geography and Ecosystem Science Lund University 223 62 Lund Sweden25Ecosystems and Environment Research Programme

Faculty of Biological and Environmental Sciences University of Helsinki 00014 Helsinki Finland26Center for Ecosystem Science and Society Northern Arizona University Flagstaff AZ 86011 USA

27Deacutepartement de Geacuteographie Universiteacute de Montreacuteal Montreacuteal QC Canada28Department of Geography and Environmental Management

University of Waterloo Waterloo ON N2L 3G1 Canada29Department of Biological Sciences University of Alberta Edmonton AB T6G 2E9 Canada

30Department of Earth Sciences and Institute for the Study of Earth Oceans and SpaceUniversity of New Hampshire Durhan NH 03824 USA

31National Boreal Program Ducks Unlimited Canada Edmonton AB T5S 0A2 Canada

Correspondence David Olefeldt (olefeldtualbertaca)

Published by Copernicus Publications

5128 D Olefeldt et al The BorealndashArctic Wetland and Lake Dataset

Received 24 April 2021 ndash Discussion started 7 May 2021Revised 21 September 2021 ndash Accepted 4 October 2021 ndash Published 5 November 2021

Abstract Methane emissions from boreal and arctic wetlands lakes and rivers are expected to increase inresponse to warming and associated permafrost thaw However the lack of appropriate land cover datasets forscaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for ourunderstanding of present-day and future methane emissions Here we present the BorealndashArctic Wetland andLake Dataset (BAWLD) a land cover dataset based on an expert assessment extrapolated using random forestmodelling from available spatial datasets of climate topography soils permafrost conditions vegetation wet-lands and surface water extents and dynamics In BAWLD we estimate the fractional coverage of five wetlandseven lake and three river classes within 05times 05 grid cells that cover the northern boreal and tundra biomes(17 of the global land surface) Land cover classes were defined using criteria that ensured distinct methaneemissions among classes as indicated by a co-developed comprehensive dataset of methane flux observationsIn BAWLD wetlands occupied 32times 106 km2 (14 of domain) with a 95 confidence interval between 28and 38times 106 km2 Bog fen and permafrost bog were the most abundant wetland classes covering sim 28 each of the total wetland area while the highest-methane-emitting marsh and tundra wetland classes occupied5 and 12 respectively Lakes defined to include all lentic open-water ecosystems regardless of size cov-ered 14times 106 km2 (6 of domain) Low-methane-emitting large lakes (gt 10 km2) and glacial lakes jointlyrepresented 78 of the total lake area while high-emitting peatland and yedoma lakes covered 18 and 4 respectively Small (lt 01 km2) glacial peatland and yedoma lakes combined covered 17 of the total lake areabut contributed disproportionally to the overall spatial uncertainty in lake area with a 95 confidence intervalbetween 015 and 038times 106 km2 Rivers and streams were estimated to cover 012 times 106 km2 (05 of do-main) of which 8 was associated with high-methane-emitting headwaters that drain organic-rich landscapesDistinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLDdomain allowing for the mapping of ldquowetscapesrdquo that have characteristic methane emission magnitudes andsensitivities to climate change at regional scales With BAWLD we provide a dataset which avoids double-accounting of wetland lake and river extents and which includes confidence intervals for each land cover classAs such BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling effortsfor the northern boreal and arctic region in particular those aimed at improving assessments of current and futuremethane emissions Data are freely available at httpsdoiorg1018739A2C824F9X (Olefeldt et al 2021)

1 Introduction

Emissions of methane (CH4) from abundant wetlands lakesand rivers located in boreal and arctic regions are expectedto substantially increase this century due to rapid climatewarming and associated permafrost thaw (Walter Anthonyet al 2018 Ito 2019 Hugelius et al 2020 Schneider vonDeimling et al 2015 Zhang et al 2017) However pre-dicting future CH4 emissions is highly uncertain as esti-mates of present-day CH4 emissions from boreal and arc-tic regions are poorly constrained ranging between 21 and77 Tg CH4 yrminus1 (Saunois et al 2020 Peltola et al 2019Wik et al 2016 Treat et al 2018 McGuire et al 2012Watts et al 2014 Thompson et al 2018 Zhu et al 2015Tan et al 2016 Walter Anthony et al 2016) Estimatesof high-latitude CH4 emissions vary between approacheswith generally lower estimates from atmospheric inversions(top-down estimates) than from field-measured CH4 emis-sions data paired with land cover data (bottom-up estimates)(Saunois et al 2020 McGuire et al 2012) Low accuracyof high-latitude land cover datasets for wetland and lake dis-

tributions and their classification represents a key source ofuncertainty for estimates of high-latitude CH4 emissions andmay contribute to the discrepancies between bottom-up andtop-down estimates A limitation of many currently avail-able land cover datasets is an insufficient differentiation be-tween wetland lake and river classes that are known to havedistinct CH4 emissions (Bruhwiler et al 2021 Bohn et al2015 Marushchak et al 2016 Melton et al 2013)

There are several challenges when using remote sensingapproaches to map distinct wetland lake and river classes atthe circumpolar scale Many small or narrow wetland ecosys-tems with high methane CH4 emissions are located alonglake shorelines along stream networks or in polygonal tun-dra terrain and are thus difficult to map as image resolutioncan be inadequate (Wickland et al 2020 Cooley et al 2017Virtanen and Ek 2014 Liljedahl et al 2016) Wetland de-tection can further be complicated by the presence of treespecies in wetlands eg Scots pine (Pinus sylvestris) blackspruce (Picea mariana) and tamarack (Larix laricina) thatare also found in non-wetland boreal forests making differ-entiation of treed wetlands from non-wetland forests diffi-

Earth Syst Sci Data 13 5127ndash5149 2021 httpsdoiorg105194essd-13-5127-2021

D Olefeldt et al The BorealndashArctic Wetland and Lake Dataset 5129

cult Using spectral signatures to differentiate and map dis-tinct wetland classes can further be difficult due to seasonalvariation in inundation or phenology poor differentiation be-tween ecosystems (eg similarities between different peat-land classes) or high spectral diversity within classes dueto shifts in vegetation along subtle environmental gradients(Raumlsaumlnen and Virtanen 2019 Vitt and Chee 1990 Chas-mer et al 2020) Vegetation composition and spectral signa-tures of wetland classes can also vary between different high-latitude regions eg with shifts in dominant tree and shrubspecies between North America and Eurasia (Raynolds etal 2019) and be influenced for decades by wildfires (Chenet al 2021 Helbig et al 2016) Active microwave remotesensing can help detect inundated wetlands and saturatedsoils but has limitations due to its computational require-ments coarse resolution and issues with detecting rarely in-undated peatlands (Beck et al 2021 Duncan et al 2020)Accurate mapping of wetlands that includes differentiationamong distinct wetland classes requires substantial groundtruthing something which has only been done consistentlyat local and regional scales (Terentieva et al 2016 Chasmeret al 2020 Bryn et al 2018 Lara et al 2018 CanadianWetland Inventory Technical Committee 2016) Similar is-sues arise for lakes rivers and streams While larger lakesand rivers have been mapped with high precision (Messageret al 2016 Linke et al 2019) the highest CH4 emissionsare generally from ponds pools and low-order streams thatare too small to be accurately detected by anything other thanvery high-resolution imagery (Muster et al 2017) Statisticalapproaches are often used to model the distribution and abun-dance of small open-water ecosystems yielding large uncer-tainties (Holgerson and Raymond 2016 Cael and Seekell2016 Muster et al 2019) Remote sensing approaches arealso inadequate in assessing other key variables known to in-fluence lake CH4 emissions including lake genesis depthand sediment characteristics (Messager et al 2016 Bro-sius et al 2021 Smith et al 2007 Lara and Chipman2021) Another key issue is that wetlands and lakes often aremapped separately allowing for potential double-counting ofecosystems in both wetland and lake inventories (Thornton etal 2016 Saunois et al 2020)

Emissions of CH4 from boreal and arctic ecosystems rangefrom uptake to some of the highest emissions observed glob-ally (Turetsky et al 2014 Knox et al 2019 Glagolev et al2011 St Pierre et al 2019) Net ecosystem CH4 emissionsare a balance between microbial CH4 production (methano-genesis) and oxidation (methanotrophy) a balance further in-fluenced by the dominant transport pathway diffusion ebul-lition and plant-mediated transport (Bridgham et al 2013Bastviken et al 2004) For wetlands defined as ecosystemswith temporally or permanently saturated soils and biotaadapted to anoxic conditions CH4 emissions in boreal andarctic regions are primarily influenced by water table posi-tion soil temperatures and vegetation composition and pro-ductivity (Olefeldt et al 2013 Treat et al 2018) Marshes

and tundra wetlands are characterized by frequent or perma-nent inundation and dominant graminoid vegetation that en-hance methanogenesis and facilitate plant-mediated transportand thus generally have high CH4 emissions (Knoblauch etal 2015 Juutinen et al 2003) Conversely peat-formingbogs and fens generally have a water table at or belowthe soil surface and their vegetation is more dominated bymosses lichens and shrubs resulting in typically low tomoderate CH4 emissions (Bubier et al 1995 Pelletier et al2007) Permafrost conditions in peatlands can cause the sur-face to be elevated and dry with cold soil conditions wheremethanogenesis is inhibited leading to low CH4 emissions oreven uptake (Baumlckstrand et al 2008 Glagolev et al 2011)Non-wetland boreal forests and tundra ecosystems gener-ally have net CH4 uptake as methanotrophy outweighs anymethanogenesis (Lau et al 2015 Juncher Joslashrgensen et al2015 Whalen et al 1992) The transition from terrestrial toaquatic ecosystems is not always well defined and severalwetland classification systems consider shallow open-waterecosystems as a distinct wetland class (Rubec 2018) Thetransition from vegetated to open-water ecosystems is how-ever associated with shifts in apparent primary controls ofCH4 emissions including a shift towards increased impor-tance of ebullition (Bastviken et al 2004) For lakes whendefined to include all lentic open-water ecosystems regard-less of size (eg including peatland ponds) spatial variabil-ity in CH4 emissions is primarily linked to water depth andthe quantity and origin of the organic matter of the sediment(Heslop et al 2020 Li et al 2020) As such lake CH4 emis-sions are generally higher for smaller lakes and for lakeswith organic-rich sediments (Wik et al 2016 Holgersonand Raymond 2016) which are extremely abundant in manyhigh-latitude regions (Muster et al 2017) The CH4 emittedfrom streams and rivers is largely derived from the soils thatare drained and as such emissions generally are higher insmaller streams draining wetland-rich watersheds (Wallin etal 2018 Stanley et al 2016) It is overall likely that studiesof CH4 emissions from boreal and arctic ecosystems havefocused disproportionally on sites with higher CH4 emis-sions (Olefeldt et al 2013) A focus on high-emitting sites iswarranted for understanding site-level controls on CH4 emis-sions but may potentially cause bias of bottom-up CH4 scal-ing approaches if they lack appropriate differentiation be-tween various wetland and lake classes in land cover datasets

There is currently no spatial dataset available that has in-formation on the distribution and abundance of wetland lakeand river classes defined specifically for the purpose of esti-mating boreal and arctic CH4 emissions However a largenumber of spatial datasets have partial but relevant infor-mation This includes circumpolar spatial data of soil types(Hugelius et al 2013 Strauss et al 2017) vegetation (Ol-son et al 2001 Walker et al 2005) surface water extentand dynamics (Pekel et al 2016) lake sizes and numbers(Messager et al 2016) topography (Gruber 2012) climate(Fick and Hijmans 2017) permafrost conditions (Gruber

httpsdoiorg105194essd-13-5127-2021 Earth Syst Sci Data 13 5127ndash5149 2021

2012 Brown et al 2002) river networks (Linke et al 2019)and previous estimates of total wetland cover (Matthews andFung 1987 Bartholomeacute and Belward 2005) By integratingquantitative spatial data with expert knowledge it is possibleto model new spatial data for specific purposes (Olefeldt etal 2016) Researchers with interests in the boreal and arc-tic have considerable knowledge of the presence and rela-tive abundance of typical wetland and lake classes in varioushigh-latitude regions along with the ability to interpret satel-lite imagery and the judgement to define parsimonious landcover classes suitable for CH4 scaling

Here we present the BorealndashArctic Wetland and LakeDataset (BAWLD) an expert-knowledge-based land coverdataset A companion dataset with chamber and small-scaleobservations of CH4 emissions (BAWLD-CH4) is presentedin Kuhn et al (2021) and it uses the same land cover classesas BAWLD The land cover classes were developed to dis-tinguish between classes with distinct CH4 emissions andinclude five wetland seven lake and three river classes InBAWLD coverage of each wetland lake and river classwithin 05 grid cells was modelled through random for-est regressions based on expert assessment data and avail-able relevant spatial data The approach aims to reduce is-sues with bias in representativeness of empirical data to re-duce issues of overlaps in wetland and lake extents and toallow for the partitioning of uncertainty in CH4 emissionsto CH4 emission magnitudes or areal extents of differentland cover classes As such BAWLD will facilitate improvedbottom-up estimates of high-latitude CH4 emissions and willbe suitable for use in process-based models and as an a pri-ori input to inverse modelling approaches The land coverdataset will be suitable for further uses especially for ques-tions related to high-latitude hydrology and biogeochemistryLastly BAWLD allows for the definition of ldquowetscapesrdquo re-gions with distinct co-occurrences of specific wetland andlake classes and which thus can be used to understand re-gional responses to climate change and as a way to visualizethe landscape diversity of the boreal and arctic domain

2 Development of the BorealndashArctic Wetland andLake Dataset

21 Study domain and harmonization of available spatialdata

The BAWLD domain includes all of the northern boreal andtundra ecoregions and also areas of rock and ice at latitudesgt 50 N (Olson et al 2001) The BAWLD domain thus cov-ers 255times 106 km2 or 17 of the global land surface Al-though northern peat-forming wetlands can also be foundin temperate ecoregions our decision to define the southernlimit of BAWLD by the transition from boreal to temperateecoregions was based on the greater human footprint and theincreased biogeographic diversity of temperate ecoregionswhich would require additional land cover classes (Venter et

Table 1 Description of data sources and layers extracted into theBAWLD 05 grid cell network

Dataset spatial resolution and extracted layers

Reference informationndash LAT latitude ()ndash LONG longitude ()ndash SHORE coastal shoreline presence in cell (yesno)

WorldClim V2 (Fick and Hijmans 2017)Spatial resolution sim 10 km

ndash WC2-MAAT mean annual average air temperature 1970ndash2000 (C)ndash WC2-MAAP mean annual average precipitation 1970ndash2000 (mm)ndash WC2-CMI climate moisture index 1970ndash2000 (mm)

Circum-Arctic Map of Permafrost and Ground-Ice (Brown et al 2002)Spatial resolution polygons of variable area

ndash CAPG-CON continuous permafrost ()ndash CAPG-DIS discontinuous permafrost ()ndash CAPG-SPO sporadic permafrost ()ndash CAPG-ISO isolated permafrost ()ndash CAPG-XHF land with thick overburden and gt 20 ground-ice ()ndash CAPG-XMF land with thick overburden and 10 ndash20 ground-ice ()ndash CAPG-XLF land with thick overburden and lt 10 ground-ice ()ndash CAPG-XHR land with thin overburden and gt 10 ground-ice ()ndash CAPG-XLR land with thin overburden and lt 10 ground-ice ()ndash CAPG-REL land with relict permafrost ()

BasinATLAS (Linke et al 2019)Spatial resolution polygons of variable area

ndash BAS-RIV river area ()

Circumpolar Arctic Vegetation Map (CAVM Team 2003)Spatial resolution polygons of variable area

ndash CAVM-BAR barren tundra ()ndash CAVM-GRA graminoid tundra ()ndash CAVM-SHR shrubby tundra ()ndash CAVM-WET wet tundra ()

HydroLakes (Messager et al 2016)Spatial resolution polygons of variable area

ndash HL-LAR lakes gt 10 km2 ()ndash HL-MID lakes between 10 and 01 km2 ()ndash HL-SHO shoreline density (lengtharea) of lakes gt 01 km2 (mm2)

Global Inundation Map (Fluet-Chouinard et al 2015)Spatial resolution sim 25 km

ndash GIM-MAMI mean annual minimum inundation ()ndash GIM-MAMA mean annual maximum inundation ()

GlobLand30 (Chen et al 2015)Spatial Resolution 30 m

ndash GL30-H2O water bodies ndash including lakes rivers reservoirs ()ndash GL30-WET wetlands ndash marshes floodplains shrub wetland peatlands ()ndash GL30-TUN tundra ndash shrub herbaceous wet and barren tundra ()ndash GL30-ART artificial surfaces ndash cities industry transport ()ndash GL30-ICE permanent snow and ice ()

Global Surface Water (Pekel et al 2016)Spatial resolution 30 m

ndash GSW-RAR rarely inundated open water in 0 to 5 of occasions ()ndash GSW-OCC occasionally inundated open water 5 to 50 ()ndash GSW-REG regularly inundated open water 50 to 95 ()ndash GSW-PER permanent open water open water 95 to 100 ()

Northern Circumpolar Soil Carbon Dataset (Hugelius et al 2014)Spatial resolution 30 m

ndash NCS-HSO histosol soils non-permafrost organic soils ()ndash NCS-HSE histel soils permafrost organic soils ()ndash NCS-AQU aqueous soils non-organic wetland soils ()ndash NCS-ROC rocklands ()ndash NCS-GLA glaciers ()ndash NCS-H2O open water ()

Global Lakes and Wetland Dataset (Lehner and Doumlll 2004)Spatial resolution polygons of variable area

ndash GLWD-RIV rivers sixth-order rivers or greater ()

Table 1 Continued

Terrestrial Ecoregions of the World (Olson et al 2001)Spatial resolution polygons of variable area

ndash Tew-bor fractional cover of boreal ecoregion ()ndash TEW-TUN fractional cover of tundra ecoregion ()ndash TEW-GLA fractional cover of glaciers ()

Global Land Cover Database 2000 (Bartholomeacute and Belward 2005)Spatial resolution sim 1 km

ndash GLC2-H2O water bodies natural and artificial ()ndash GLC2-RFSM regularly flooded shrub andor herbaceous cover ()ndash GLC2-FOR forest cover ()

Dataset of Ice-Rich Yedoma Permafrost (Strauss et al 2017)spatial resolution polygons of variable area

ndash IRYP-YED yedoma ground ()

Permafrost zonation and Terrain Ruggedness Index (Gruber 2012)Spatial resolution sim 1 km

ndash PZI-PERM permafrost ground ()ndash PZI-FLAT flat topography ()ndash PZI-UND undulating topography ()ndash PZI-HILL hilly topography ()ndash PZI-MTN mountainous topography ()ndash PZI-RUG rugged topography ()

Global Wetlands (Matthews and Fung 1987)Spatial resolution 1

ndash GWET-IN inundation and presence of wetlands ()

al 2016) A network of 05 grid cells cropped along coastsand at the transition from boreal to temperate ecoregions wascreated for the BAWLD domain

Grid cells in BAWLD were populated with data from 15publicly available spatial datasets yielding 53 variables withspatial information (Table 1) Most datasets that were in-cluded have data at higher resolution than the 05 BAWLDgrid cells hence information was averaged for each gridcell For datasets where the spatial resolution was coarseror where spatial data were not aligned with the 05 gridcells data were first apportioned into BAWLD grid cells be-fore area-weighted averages were calculated Climate datafrom the WorldClim2 (WC2) dataset (Fick and Hijmans2017) were averaged for each grid cell including ldquomeanannual air temperaturerdquo ldquomean annual precipitationrdquo andldquoclimate moisture indexrdquo Information on soils and per-mafrost conditions were summarized as fractional cover-age within each grid cell and included ldquopermafrost extentrdquofrom the Permafrost Zonation and Terrain Ruggedness Index(PZI) dataset (Gruber 2012) permafrost zonation groundice content and overburden thickness from the Circum-Arctic Map of Permafrost and Ground-Ice (CAPG) dataset(Brown et al 2002) ldquoyedoma groundrdquo from the Ice-RichYedoma Permafrost (IRYP) dataset (Strauss et al 2017)and non-permafrost peat ldquohistosolrdquo permafrost peat ldquohistelrdquoand ldquoaqueousrdquo wetland soils from the Northern CircumpolarSoil Carbon Database (NCSCD hereafter NCS) (Hugeliuset al 2013) Four independent datasets provided informa-tion on wetland coverage although without further differ-entiation between distinct wetland classes the ldquoregularly

flooded shrub andor herbaceous coverrdquo area from the GlobalLand Cover Database 2000 (GLC2) (Bartholomeacute and Bel-ward 2005) the ldquowetlandsrdquo area in the GlobLand30 (GL30)dataset (Chen et al 2015) and the ldquoinundation and pres-ence of wetlandsrdquo area from the Global Wetlands (GWET)dataset (Matthews and Fung 1987) and the Circumpolar Arc-tic Vegetation Map (CAVM) dataset (Walker et al 2005)Two datasets provided information of the extent of forestedregions ndash the GLC2 and the Terrestrial Ecoregions of theWorld (TEW) dataset (Olson et al 2001) ndash while threedatasets provided information on the extents of tundra vege-tation the CAVM the GL30 and the TEW Three datasetsprovided information on extent of glaciers and permanentsnow the NCS the GL30 and the TEW The NCS datasetalso provided information about the extents of ldquorocklandsrdquowhile the PZI dataset had extents of topographic ruggedness(ldquoflatrdquo ldquoundulatingrdquo ldquohillyrdquo ldquomountainousrdquo and ldquoruggedrdquo)Information on river extents was found in two datasets theldquoriver areardquo in the BasinATLAS (BAS) dataset (Linke et al2019) and ldquoriversrdquo in the Global Lakes and Wetland (GLW)dataset which includes sixth-order rivers and greater (Lehnerand Doumlll 2004) Inundation dynamics was provided by twodatasets with ldquomean annual minimumrdquo and ldquomean annualmaximumrdquo inundation in the Global Inundation Map (GIM)dataset (Fluet-Chouinard et al 2015) and an analysis oftemporal inundation from the Global Surface Water (GSW)dataset (Pekel et al 2016) where we defined inundation ofindividual 30 m pixels as being inundated ldquorarelyrdquo (gt 0 to5 of all available Landsat images) ldquooccasionallyrdquo (5 to50 ) ldquoregularlyrdquo (50 to 95 ) or ldquopermanentlyrdquo (95 to100 ) Four datasets included information about static ex-tents of open water including ldquoopen waterrdquo in NCS ldquowaterbodiesrdquo in GL30 ldquowater bodiesrdquo in GLC2 and informationabout lakes in the Hydrolakes (HL) dataset (Messager et al2016) where we differentiated between the area of ldquolargelakesrdquo (lakes gt 10 km2) and ldquomidsize lakesrdquo (lakes between01 and 10 km2) High-latitude data were not available for theGL30 (gt 82 N) and HL (gt 80 N) datasets and were codedas missing data Regions outside the spatial extents of theCAVM CAPG and IRYP datasets were coded as 0 as it sug-gested absence of tundra vegetation permafrost and yedomasoils

22 Land cover classes in BAWLD

The land cover classification in BAWLD was constructedwith the goal to enable upscaling of CH4 fluxes for largespatial extents As such we aimed to include as few classesas possible to facilitate large-scale mapping while still in-cluding classes that allow for separation among ecosystemswith distinct hydrology ecology biogeochemistry and thusnet CH4 fluxes The BAWLD land cover classification is hi-erarchical with five wetland classes seven lake classes andthree river classes along with four other classes glaciers drytundra boreal forest and rocklands The class descriptions

(see Kuhn et al 2021 for further details) were provided toall experts for their land cover assessments and thus effec-tively serve as the BAWLD class definitions

221 Wetland classes

Wetlands are defined by having a water table near or abovethe land surface for sufficient time to cause the developmentof wetland soils (either mineral soils with redoximorphic fea-tures or organic soils with gt 40 cm peat) and the presence ofplant species with adaptations to wet environments (Hugeliuset al 2020 Canada Committee on Ecological (Biophysical)Land Classification et al 1997 Jorgenson et al 2001) Wet-land classifications for boreal and arctic biomes can focus ei-ther on small-scale wetland classes that have distinct hydro-logical regimes vegetation composition and biogeochem-istry or on larger-scale wetland complexes that are comprisedof distinct patterns of smaller wetland and open-water classes(Gunnarsson et al 2014 Terentieva et al 2016 Masing etal 2010 Glaser et al 2004) While larger-scale wetlandcomplexes are easier to identify through remote sensing tech-niques (eg patterned fens comprised of higher-elevationridges and inundated hollows) our classification focuses onwetland classes due to greater homogeneity of hydrologicalecological and biogeochemical characteristics that regulateCH4 fluxes (Heiskanen et al 2021)

Several boreal countries identify four main wetlandclasses differentiated primarily based on hydrodynamiccharacterization bogs fens marshes and swamps (Gunnars-son et al 2014 Canada Committee on Ecological (Biophys-ical) Land Classification et al 1997 Masing et al 2010)The BAWLD classification follows this general frameworkbut further uses the presence or absence of permafrost as aprimary characteristic for classification and excludes a dis-tinct swamp class yielding five classes Bogs Fens MarshesPermafrost Bogs and Tundra Wetlands (Fig 1) A swampclass was omitted due to the wide range of moisture and nu-trient conditions of swamps as well as the limited numberof studies of swamp CH4 fluxes (Kuhn et al 2021) We in-stead included swamp ecosystems in expanded descriptionsof Bogs Fens and Marshes The presence or absence ofnear-surface permafrost was used as a primary characteris-tic to distinguish between Permafrost Bogs and Bogs and todistinguish Tundra Wetlands from Marshes and Fens Thepresence or absence of near-surface permafrost is consid-ered key for controlling CH4 emissions given its influenceon hydrology and for the potential of permafrost thaw andthermokarst collapse to cause rapid non-linear shifts in CH4emissions (Bubier et al 1995 Turetsky et al 2002 Mal-hotra and Roulet 2015) Detailed descriptions and defini-tions of Bogs Fens Marshes Permafrost Bogs and TundraWetlands for the purpose of BAWLD can be found in Kuhnet al (2021) Differences in moisture regimes nutrient andpH regimes hydrodynamics permafrost conditions (Fig 1)and vegetation lead to distinct vegetation assemblages among

Figure 1 Descriptions of wetland classes in BAWLD as distin-guished based on the moisture regime the nutrient and pH regimehydrodynamics and the presenceabsence of permafrost

the wetland classes While each class has large variabilityin CH4 emissions there are clear differences between mostclasses with Permafrost Bogs lt Bogs lt Fens=Tundra Wet-lands lt Marshes (Kuhn et al 2021)

222 Lake classes

Lakes in BAWLD are considered to include all lentic open-water ecosystems regardless of surface area and depth ofstanding water It is common in tundra lowlands and peat-land regions for open-water bodies to have shallow depthsoften less than 2 m even when surface areas are up to hun-dreds of square kilometres in size (Grosse et al 2013)While small shallow open-water bodies often are includedin definitions of wetlands (Canada Committee on Ecolog-ical (Biophysical) Land Classification et al 1997 Gun-narsson et al 2014 Treat et al 2018) we include themhere within the lake classes as controls on net CH4 emis-sions depend strongly on the presence or absence of emer-gent macrophytes (Juutinen et al 2003) Further classifi-cation of lakes in BAWLD is based on lake size and lakegenesis where lake genesis influences lake bathymetry andsediment characteristics Previous global spatial invento-ries of lakes include detailed information on size and lo-cation of individual lakes (Messager et al 2016 Down-ing et al 2012) but do not include open-water ecosys-tems lt 01 km2 in size and do not differentiate betweenlakes of different genesis (eg tectonic glacial organicand yedoma lakes) Small water bodies are disproportion-ately abundant in some high-latitude environments (Musteret al 2019) have high emissions of CH4 (Holgerson andRaymond 2016) and therefore require explicit classificationapart from larger water bodies Furthermore lake genesis and

sediment type haven been shown to influence net CH4 fluxfrom lakes (Wik et al 2016) In BAWLD we thus differ-entiate between large (gt 10 km2) midsize (01 to 10 km2)and small (lt 01 km2) lake classes and further differenti-ate between three lake types for midsize and small lakespeatland yedoma and glacial lakes Detailed descriptions ofthe seven lake classes in BAWLD can be found in Kuhn etal (2021) where it is also shown that net CH4 emissions(combined ebullitive and diffusive emissions) vary amongclasses with large Lakes lt Midsize Glacial Lakes= SmallGlacial Lakes lt Midsize Yedoma Lakes lt Midsize PeatlandLakes lt Small Peatland Lakes= Small Yedoma Lakes

223 River classes

We include three river classes in BAWLD Large RiversSmall Organic-Rich Rivers and Small Organic-Poor RiversLarge rivers are described as sixth-Strahler-order rivers orgreater and generally have river widths gtsim 75 m (Down-ing et al 2012 Lehner and Doumlll 2004) Small Organic-Rich Rivers include all first- to fifth-order streams and riversthat drain peatlands or other wetland soils thus being asso-ciated with high concentrations of dissolved organic carbonand high supersaturation of CH4 Conversely Small Organic-Poor Rivers drain regions with fewer wetlands and organic-rich soils and generally have lower concentrations of dis-solved organic carbon and dissolved CH4

224 Other classes

Four additional classes are included in BAWLD GlaciersRocklands Dry Tundra and Boreal Forests Glaciers includeboth glaciers and other permanent snow and ice on landRocklands include areas with very poor soil formation andwhere vegetation is largely absent Rocky outcrops in shieldlandscapes slopes of mountains and high-arctic barren land-scapes are included in the class The Rocklands class also in-cludes artificial surfaces such as roads and towns Glaciersand Rocklands are considered to be close to neutral with re-spect to CH4 emissions The Dry Tundra class includes bothlowland arctic tundra and alpine tundra both treeless ecosys-tems dominated by graminoid or shrub vegetation Dry Tun-dra ecosystems generally have near-surface permafrost withseasonally thawed active layers between 20 and 150 cm de-pending on climate soil texture and landscape position (vander Molen et al 2007 Heikkinen et al 2004) Near-surfacepermafrost in Dry Tundra prevents vertical drainage but lat-eral drainage ensures predominately oxic soil conditions Awater table is either absent or close to the base of the season-ally thawing active layer Dry Tundra is differentiated fromPermafrost Bogs by having thinner organic soil (lt 40 cm)and from Tundra Wetlands by their drained soils Dry Tun-dra generally have net CH4 uptake but low CH4 emissionsare sometimes found (Kuhn et al 2021) Boreal Forests aretreed ecosystems with non-wetland soils Coniferous trees

are dominant but the class also includes deciduous treesin warmer climates and landscape positions Boreal Forestsmay have permafrost or non-permafrost ground where ab-sence of permafrost often allows for better drainage Overallit is rare for anoxic conditions to occur in Boreal Forest soilsand CH4 uptake is prevalent although low CH4 emissionshave been observed during brief periods during snowmelt orfollowing summer storms (Matson et al 2009) or conveyedthrough tree stems and shoots (Machacova et al 2016) TheBoreal Forest class also includes the few agricultural and pas-ture ecosystems within the boreal biome

23 Expert assessment

Expert assessments can be used to inform various environ-mental assessments and are particularly useful to assess lev-els of uncertainty and to provide data that cannot be obtainedthrough other means (Olefeldt et al 2016 Loisel et al 2021Abbott et al 2016 Sayedi et al 2020) We solicited an ex-pert assessment to aid in the modelling of fractional cover-age of the 19 land cover classes within each BAWLD gridcell Researchers associated with the Permafrost Carbon Net-work (httpwwwpermafrostcarbonorg last access 30 Oc-tober 2021) with expertise from wetland lake andor riverecosystems within the BAWLD domain were invited to par-ticipate We also included a few additional referrals to suit-able experts outside the Permafrost Carbon Network A to-tal of 29 researchers completed the expert assessment andare included as co-authors of the BAWLD dataset Each ex-pert was asked to identify a region within the BAWLD do-main for which they considered themselves familiar Expertswere then assigned 10 random cells from their region of fa-miliarity and 10 cells distributed across the BAWLD domainthat allowed for an overall balanced distribution of trainingcells (Fig S1 in the Supplement) No cell was assessed morethan once and in total sim 3 of the area of the BAWLD do-main was included in the expert assessment Each expert wasasked to assess the percent coverage of each of the 19 landcover classes within their 20 training cells To guide theirassessment each expert was provided step-by-step instruc-tions plus information on the definitions of each land coverclass and a KML file with the data extracted from availablespatial datasets for each grid cell (Table 1) Experts wereasked to use their knowledge of typical wetland and lakeclasses within specific high-latitude regions their ability tointerpret satellite imagery as provided by Google Earth andtheir judgement of the quality and relevance of available spa-tial datasets to make their assessments of fractional coverThe information provided to experts to carry out the assess-ment is provided in the Supplement

24 Random forest model and uncertainty analysis

Random forest regression models were created to predict thepercent coverage of all 19 individual BAWLD land coverclasses along with three additional models for total wet-land lake and river coverage The regression models usedthe expert assessment of land cover fractional extent as theresponse variables Each land cover class was at first mod-elled separately which was followed by minor adjustmentsdescribed below that ensured that the total land cover withineach cell added up to 100 All statistical analysis and mod-elling were done using R 402 (R Core Team 2020) andthe packages Boruta (v700 Kursa and Rudnicki 2010)caret (v60-86 Kuhn 2020) randomForest (v46-14 Liawand Wiener 2002) and factoextra (v107 Kassambara andMundt 2020)

Prior to running the random forest analyses we performedan automatic feature selection using a Boruta algorithm(Kursa and Rudnicki 2010) The Boruta algorithm com-pleted 150 runs for each land cover class after which subsetsof the 53 possible data variables (Table 1) were deemed im-portant and selected for inclusion in subsequent random for-est models (Table 2) The random forest models (Kuhn 2020Liaw and Wiener 2002) then used boot-strapped samples(ie the expert assessments of land cover fractional grid cellcoverages) to grow 500 decision trees (ntree) with a subsetof randomized data variables as predictors at each tree node(mtry) We used a 10-fold cross-validation with five repeti-tions providing mtry as a tuneable parameter for model train-ing The random forest model output included the root meansquare error (RMSE) the percent of the expert assessmentvariability that was explained (Var) and relative variableimportance (Table 2) Relative variable importance assigns a100 importance to the variable with the most influence onthe model and then ranks all other variables relative to the in-fluence of that variable A bias correction (Song 2015) wasapplied to the predicted data of land cover class coveragesas the models were found to overestimate low coverages andunderestimate high coverages After the bias correction allbias-adjusted predictions lt 0 were set to 0 while thosegt 100 were set to 100 (for examples of the bias correc-tion see Fig S2) Next we ensured that the combined cover-age of all 19 land cover classes within each grid cell added upto 100 by applying a proportional adjustment In order toestimate the 5th and 95th percentile confidence bounds of theland cover predictions we repeated the random forest analy-sis as outlined above an additional 20 times for each classEach new run completely excluded 20 of the expert assess-ments and the data were reshuffled four times Each grid cellthus had 21 predictions of coverage for each of the 19 landcover classes and for the cumulative wetland lake and rivercoverages and the variability in these predictions were usedto define the 5th and 95th percentile confidence bounds

While each cell in BAWLD has a distinct land cover com-bination we were also interested in identifying cells with

similarities in their land cover compositions to distinguishbetween regions of the boreal and arctic domain that rep-resent characteristic landscapes We carried out a k-meansclustering (Kassambara and Mundt 2020) to group grid cellswith similarities in their predicted land cover compositionsThe k-means clustering was based on within-cluster sum ofsquares and we evaluated resulting maps with between 10and 20 distinct classes Using 15 clusters was deemed to bal-ance the within-cluster sum of squares and interpretability ofthe resulting map We henceforth refer to these clusters asldquowetscapesrdquo as each cluster was defined largely by the rela-tive dominance (or absence) of different wetland lake andriver classes

25 Evaluation against regional wetland datasets

We evaluated the predictions of wetland coverage in BAWLDagainst four independent high-resolution regional land coverdatasets These four datasets were chosen as they includedmore than one wetland class thus enabling evaluation againstboth total wetland coverage and subsets of wetland classesTwo of these datasets were specifically aimed at mapping ofwetlands including Ducks Unlimited Canadarsquos wetland in-ventories for western Canada as part of the Canadian Wet-land Inventory (CWI Canadian Wetland Inventory TechnicalCommittee 2016) and wetland mapping of the West SiberianLowlands (WSL) (Terentieva et al 2016) The other twodatasets the 2016 National Land Cover Database (NLCD)of Alaska (Homer et al 2020) and the 2018 CORINE LandCover (CLC) (Buumlttner 2014) of northern Europe repre-sent more general land cover datasets Data from these fourdatasets were summarized for each BAWLD grid cell wherethere was complete coverage Data filtration was done forthe CWI to remove cells if gt 10 of the cell was classifiedas burned cloud or shadow There were few cases wherethere were equivalent wetland classes in BAWLD and thesefour regional datasets and as such comparisons were gener-ally made between groups of wetland classes that were con-sidered generally comparable Similar evaluations were notpossible for the lake classes as there are no regional or cir-cumpolar spatial datasets with information on lake genesis orsediment type

3 Results and discussion

The fractional land cover estimates of the BorealndashArctic Wet-land and Lake Dataset (BAWLD) are freely available on-line at httpsdoiorg1018739A2C824F9X (Olefeldt et al2021) and include both the central estimates and the 95 high and low estimates of each land cover class in each gridcell

Table 2 Summary of random forest models for each land cover class in BAWLD

Land cover classes RMSE () Var mtrya Varb Relative variable importancec

Glaciers 232 959 13 24 GL30-ICE (100) NCS-GLA (6)

Rocklands 979 672 21 41 NCS-ROC (100) PZI-MTN (43) CAVM-BAR (39)PZI-RUG (24) CAPG-XLR (16) WC2-MAAP (14) WC2-MAAT (14)WC2-CMI (11) TEW-TUN (10) GLC2-FOR (10)

Tundra 147 752 22 43 TEW-TUN (100) TEW-BOR (41) PZI-PERM (26) GL30-TUN (16)WC2-MAAP (8) CAPG-CON (7) LAT (7) PZI-PERM (7) NCS-ROC(5)

Boreal Forest 155 798 20 39 GLC2-FOR (100) TEW-BOR (61) GL30-WET (23) TEW-TUN (21)GIEMS_MAMA (12) GIM_MAMI (11) GL30_TUN (10)

Wetland classes 85 858 25 48 GL30-WET (100) NCS-HSE (46) NCS-HSO (44) PZI_FLAT (28)GWET-IN (10) WC2-MAAT (6) GLC2-WET (5)

Bog 47 750 22 42 NCS-HSO (100) GL30-WET (47) WC2-MAAT (23) PZI-PERM (17)PZI_FLAT (12) GLC2-WET (12) WC2-MAAP (6)

Fen 45 763 21 40 NCS-HSO (100) GL30-WET (56) WC2-MAAT (16) PZI-PERM (7)Marsh 13 541 18 34 GL30-WET (100) GSW-OCC (80) GLC2-WET (56) BAS-RIV (31)

NCS-HSO (17) WC2-MAAT (14) GLWD-RIV (13) PZI-PERM (12)IRYP-YED (10) GSW-RAR (10)

Permafrost Bog 41 840 22 42 NCS-HSE (100) CAPG_DIS (6) CAPG-XMF (5)Tundra Wetland 41 472 2 36 CAVM-WET (100) GSW-OCC (95) NCS-HSE (80) CAPG-XHF (77)

PZI-FLAT (63) GL30-TUN (61) WC2-CMI (57) HL-MID (57)IRYP-YED (56) LAT (53)

Lentic classes 203 978 32 32 GL30-H2O (100)Large Lake 075 995 30 30 HL-LAR (100) GL30-H2O (18) NCS-H2O (10)Midsize Glacial Lake 149 753 18 35 HL-SHO (100) HL-MID (56) GSW-REG (18) GL30-H2O (8)

NCS-H2O (8) GSW-PER (6) NCS-ROC (5) GLC2-WET (5)PZI-PERM (5)

Midsize Peatland Lake 144 685 17 32 HL-MID (100) NCS-HSO (24) GL30-WET (18) GWET-IN (18)HL-SHO (17) NCS-HSE (14) GSW-OCC (13) GLC2-WET (11)GIM-MAMI (9) GSW-PER (8)

Midsize Yedoma Lake 086 684 16 31 IRYP-YED (100) HL-MID (42) HL-SHO (23) CAVM-WET (14)CAPG-XHF (12) GSW-OCC (12) PZI-PERM (9) WC2-CMI (8)GL30-H2O (7) GSW-REG (6)

Small Glacial Lake 089 156 2 29 GSW-OCC (100) GSW-REG (81) HL-SHO (49) HL-MID (39)GIM-MAMA (37) GIM-MAMI (29) GSW-RAR (27) WC2-MAAT (26)PZI-PERM (25) GL30-H2O (23)

Small Yedoma Lake 047 392 17 32 IRYP-YED (100) WC2-MAAP (25) WC2-CMI (21) GLC2-H2O (15)GSW-OCC (14) CAVM-WET (14) PZI-FLAT (13) GSW_REG (11)CAPG-XHF (10) HL-MID (10)

Small Peatland Lake 122 659 20 39 GLC2-WET (100) GL30-WET (95) WC2-MAAT (34) CAPG-REL (32)NCS-HSE (26) PZI-PERM (24) NCS-HSO (23) GSW_OCC (23)LAT (17) WC2-CMI (16)

Lotic classes 049 903 16 31 GLWD-RIV (100) BAS-RIV (39) GSW-OCC (11)Large River 048 904 17 32 GLWD-RIV (100) BAS-RIV (39) GSW-OCC (10)Small Organic-Poor Rivers 009 187 2 41 GSW-OCC (100) GLC2-WET (62) BAS-RIV(48) GSW-PER (42)

GLWD-RIV (39) PZI-FLAT (38) GL30-H2O (33) GLC2-H2O (32)GIM-MAMA (32) WC2-MAAP (31)

Small Organic-Rich Rivers 004 593 23 45 GLC3-WET (100) PZI-FLAT (41) NCS-HSE (37) NCS-HSO (18)GLC2-WET (17) GWET-IN (13) GSW-OCC (12) CAPG-XLR (12)GLWD-RIV (11) BAS-RIV (11)

a mtry is a fitted variable which decides how many variables were randomly chosen at each split in the random forest analysis b Var indicates the number of variables thatwere included (out of 53) in the random forest analysis after the Boruta automatic feature selection c Relative variable importance ndash the most influential variable in the randomforest analysis is assigned a 100 rating and the importance of other variables is relative to this See Table 1 for full descriptions of the variables Here we list either all variableswith gt 5 influence or the top 10 variables

31 Wetlands

Wetlands were predicted to cover a total of 32times 106 km2or 125 of the BAWLD domain The wetland area wasdominated by Fens (29 of total wetland area) Bogs(28 ) and Permafrost Bogs (27 ) while Marshes andTundra Wetlands which have relatively higher CH4 emis-sions covered 5 and 12 of the wetland area respec-tively (Table 3) This estimate of total wetland area wasgreater than previously mapped within the BAWLD domainin GLC2 at 09times 106 km2 (Bartholomeacute and Belward 2005)GL30 at 14times 106 km2 (Chen et al 2015) and GWET at23times 106 km2 (Matthews and Fung 1987) but similar to thearea of wetland soils in NCS (sum of ldquohistosolsrdquo ldquohistelsrdquoand ldquoaqueousrdquo soil coverage) at 30times 106 km2 (Hugelius etal 2014) Differences between BAWLD and other estimatesof wetland area likely stem partially from differences in wet-land definitions where for example definitions of wetlandsin GLC2 and GL30 likely do not include wooded bogsfens and permafrost bogs While estimates of total wetlandarea GWET and in the NCS were closer to BAWLD therewere differences in the spatial distribution Wetland cover inBAWLD was generally greater than in GWET and NCS in re-gions with low wetland cover This likely reflects the abilityof experts to infer the presence of small or transitional wet-lands that may otherwise be underestimated when mappedusing other methodologies Conversely wetland cover inBAWLD was generally lower than in GWET and NCS inregions with high wetland cover This was likely due to dif-ferences in definitions especially the exclusion of all open-water ecosystems from wetlands in BAWLD For example itwas common in the West Siberian Lowlands for the summedcoverage of wetland soils in NCS and the ldquoopen-waterrdquo cov-erage in GL30 to be substantially greater than 100 sug-gesting that NCS included peatland pools and small pondswithin its wetland soil coverage Overall the predictive ran-dom forest model of total wetland coverage was able to ex-plain 86 of the variability in the expert assessments and itwas primarily influenced by the area of ldquowetlandsrdquo in GLC30and the wetland soil categories in NCS followed by the cov-erage of ldquoflat topographyrdquo in PZI (Gruber 2012) (Table 2)

The predictive random forest models for individual wet-land classes differed both in terms of how much of thevariability in the expert assessment data was explained andin terms of which spatial data were most influential (Ta-ble 2) The model for Permafrost Bog coverage explained84 of the variability in the expert assessments and wasvery strongly influenced by ldquohistelrdquo distribution in the NCS(Hugelius et al 2014) Predictive models explained sim 75 of the variability in the expert assessments for Bogs and Fensseparately (Table 2) but 87 when considered jointly Thisshows that the available predictor variables were less suit-able for modelling Bogs and Fens separately than jointlywhich could partly be due to lower agreement among ex-perts in assessments of Bog and Fen coverages compared

to their sum This would not be surprising as bogs and fens(and swamps) occur along hydrological and nutrient gradi-ents and can have vegetation characteristics that make themdifficult to distinguish Models for Bogs and Fens were bothstrongly influenced by the ldquohistosolrdquo distribution in NCSwith secondary influences from the area of ldquowetlandsrdquo inGL30 ldquopermafrost extentrdquo in PZI and ldquomean annual air tem-peraturerdquo in WC2 Predictive models for Marsh and TundraWetlands explained less of the variability in expert assess-ments at 54 and 47 respectively The predictive mod-els for Marsh and Tundra Wetlands were influenced by vari-ables that indicate a transition between terrestrial and aquaticecosystems eg area of ldquooccasional inundationrdquo in GSWldquoriversrdquo in BAS and ldquomidsize lakesrdquo in HL but then differedin the influence of climate and permafrost conditions

Each wetland class had a distinct spatial distribution(Fig 2) Bogs and Fens were the dominant wetland classesin relatively warmer climates with high densities in the WestSiberian Lowlands Hudson Bay Lowlands and the Macken-zie River Basin While Bogs and Fens had similarities intheir spatial distributions there was also a relative shift indominance from Bogs to Fens in relatively colder and drierclimates (Fig S3) These trends are supported by bog-to-fen transitions observed both within and between regions(Packalen et al 2016 Vitt et al 2000a Vaumlliranta et al2017) but may not be universal (Kremenetski et al 2003)Marshes were also found in warmer climates and largely as-sociated with Bogs and Fens but with a more evenly spreaddistribution The highest abundance of Marsh coverage waspredicted for the Ob River floodplains a region with veryfew field studies of CH4 emissions (Terentieva et al 2019Glagolev et al 2011) Bogs Fens and Marshes all decreasedin abundance in colder climates with Permafrost Bogs be-coming more abundant than Bogs when mean annual temper-atures were below minus25 C corresponding to findings fromwestern Canada Fennoscandia and the West Siberian Low-lands (Vitt et al 2000b Seppaumllauml 2011 Terentieva et al2016) Tundra Wetlands became dominant over Fens andMarshes when mean annual air temperatures were belowminus55 C (Fig 3) Tundra Wetlands were predicted to be mostabundant in the lowland regions across the Arctic Oceancoast with especially high abundance in northern Alaskaeastern Siberia and on the Yamal and Gydan peninsulas inwestern Siberia

We found good agreement between the distribution of wet-lands in BAWLD and that of four independent regional spa-tial datasets (Figs 4 S4) The best agreements for total wet-land cover were between BAWLD and the two datasets ded-icated specifically to wetland mapping with R2 of 076 withthe WSL dataset and 072 with the CWI dataset There werealso strong relationships between BAWLD and the WSLdataset for the distribution of specific wetland classes forboth drier wetland classes (ldquoridgerdquo+ ldquoryamrdquo+ ldquopalsardquo vsPermafrost Bog+Bog) and wetter classes (ldquofenrdquo+ ldquohollowrdquovs Fen) When comparing ldquowet hollowrdquo of the WSL dataset

Table 3 Summary of central estimates 95 low and high confidence bounds and the range of the 95 confidence interval expressed as apercent of the central estimate for each of the land cover classes within the BAWLD domain

Land cover classes Central Low High 95 95 CIestimate confidence confidence confidence (percent of central

(106 km2) bound bound interval estimate)(106 km2) (106 km2) (106 km2)

Glaciers 209 199 221 022 11Rocklands 274 221 340 119 44Tundra 528 456 637 182 34Boreal Forest 1066 977 1139 161 15

Wetlands 318 279 379 100 31Bog 088 071 124 053 60Fen 091 076 114 038 42Marsh 016 012 023 011 71Permafrost Bog 086 067 117 050 58Tundra Wetland 038 031 053 022 59

Lakes 144 134 159 024 17Large Lake 064 061 072 011 18Midsize Peatland Lake 014 011 021 010 69Midsize Yedoma Lake 0034 0023 0071 005 140Midsize Glacial Lake 038 033 043 010 26Small Peatland Lake 012 0085 017 008 71Small Yedoma Lake 0028 0015 0046 003 114Small Glacial Lake 0094 0051 016 011 119

Rivers 012 0094 019 010 81Large River 0080 0072 011 004 50Small Organic-Rich Rivers 0010 0005 0054 005 502Small Organic-Poor Rivers 0033 0020 0067 005 143

and Marsh in BAWLD there were discrepancies but theywere primarily attributed to the explicit exclusion of theOb River floodplains in the WSL dataset (Fig S4) For thewettest classes we had only a weak relationship (R2

= 019)between the CWI ldquomarshrdquo class and the sum of the BAWLDMarsh and Tundra Wetland classes but the overall averageabundance for comparable grid cells was similar at 14 and 22 respectively Agreements between BAWLD andthe NLCD and CLC datasets were lower especially forthe relatively drier wetland classes (Fig S4) Lower agree-ment between BAWLD and some classes of regional wet-land datasets should not be interpreted to demonstrate pooraccuracy of BAWLD as differences can be due to class def-initions large mapping units and relatively low accuracy ofthe non-wetland-specific regional datasets

The 95 confidence intervals for predictions of abun-dance varied both between wetland classes and among re-gions (Table 3 Figs S5 S6) The confidence interval for totalwetland area was between 28 and 38times 106 km2 ie a rangethat represented 31 of the central estimate The range ofthe confidence interval depends both on how much consensusthere is among experts in their assessments and how well theavailable spatial datasets used in the random forest modelling

can explain the expert assessments The considerable rangeof the confidence interval for wetlands likely stems from acombination of these two components The confidence in-terval for total area of individual wetland classes varied be-tween representing 42 (Fens) and 71 (Marshes) of re-spective central estimates The absolute range of confidenceintervals for individual cells generally increased with highercentral estimates of abundances but the range of confidenceintervals decreased if expressed as a percent of the centralestimate (Fig S7)

32 Lakes

Lakes were predicted to cover a total of 144 times 106 km2or 56 of the BAWLD domain Large Lakes had thegreatest lake area (44 of total lake area) followed byMidsize Glacial Lakes (26 ) and Midsize Peatland Lakes(10 ) (Table 3) The lake classes with the highest CH4emissions Small Yedoma Lakes and Small Peatland Lakesjointly covered 10 of the total lake area The total pre-dicted lake area in BAWLD was higher than the area oflakes in HL (120times 106 km2) which only includes lakesgt 01 km2 and was similar to the area of ldquoopen waterrdquoin GL30 (143times 106 km2) The ldquoopen-waterrdquo class in the

Figure 2 Predicted distribution of wetland classes across the BAWLD domain (a) Bog (b) Fen (c) Marsh (d) Permafrost Bog and(e) Tundra Wetland

Figure 3 Relative abundance of the five wetland classes across agradient of mean annual temperatures

GL30 dataset is however based on Landsat 30 m resolu-tion data and thus excludes very small open-water areaswhile it includes both lentic and lotic open water The 95 confidence interval for the total lake area in BAWLD was024times 106 km2 or 17 of the central estimate

The predictive models for the three midsize lake classeseach explained between 69 and 75 of the variabilityin expert assessments while a model for the sum of thethree midsize lake classes explained 991 The predictivemodel for the sum of the three midsize lake classes was al-most exclusively influenced by the area of ldquomidsize lakesrdquo

in HL while the three midsize lake classes were differen-tiated through further influences by the area of ldquoyedomagroundrdquo (Midsize Yedoma Lakes) by the area of ldquohistosolsrdquoand ldquohistelsrdquo in NCS and ldquowetlandsrdquo in GL30 (MidsizePeatland Lakes) and by ldquoshoreline lengthrdquo in HL (MidsizeGlacial Lakes) The influence of ldquoshoreline lengthrdquo for Mid-size Glacial Lakes shows that experts associated glacial lakeswith high shoreline development and peatland and yedomalakes with low shoreline development Despite similaritiesin how much of the expert assessments were explained bythe predictive models (69 ndash75 ) the extrapolation to theBAWLD domain led to large differences in the 95 con-fidence interval which represented only 26 of the cen-tral estimate for Midsize Glacial Lakes while representing69 and 140 for Midsize Peatland and Midsize YedomaLakes respectively (Table 3 Fig S8) Midsize Glacial Lakeswere predominately predicted to have high abundances onthe Canadian Shield and in Fennoscandia while MidsizeYedoma Lakes were associated with the lowland coastal tun-dra regions of northeastern Siberia and Alaska and MidsizePeatland Lakes were especially common in the West SiberianLowlands but also common in the peatland regions of theHudson Bay Lowlands the Mackenzie River Basin and incoastal lowland regions (Fig 5)

Small Glacial Yedoma and Peatland Lakes were jointlyestimated to cover 09 of the BAWLD domain The pre-dictive models explained 16 39 and 66 of the vari-

Figure 4 Comparison of total wetland extent between BAWLD and four regional independent wetland inventories the National Land CoverDatabase (NLCD) the Canadian Wetland Inventory (CWI) the wetland mapping of the West Siberian Lowlands (WSL) and the CORINELand Cover (CLC) dataset (a) Spatial extents of the regional datasets (b) correlations between grid cell wetland coverages in BAWLD andthe regional datasets (c) spatial distribution of total wetland coverages in the four regional datasets (d) spatial distribution of total wetlandcoverage in BAWLD for grid cells corresponding with the regional datasets

ability in the expert assessments respectively (Table 2) Therelatively lower predictive power for small lakes was notunexpected given the lack of information on the smallestopen-water systems in the available spatial data the variableabundance of very small open-water systems among land-scapes (Muster et al 2019) and a lower relative consensusamong experts when assessing classes with generally smallfractional coverages Models for all three small lake classeswere influenced by the area of ldquooccasional inundationrdquo inGSW but were then differentiated by variables largely sim-ilar to those that were characteristic of the correspondingmidsize lake classes (Table 2) The predicted distributions ofthe small lake classes were also largely similar to that of thecorresponding midsize lake type classes (Fig 5) The over-all predicted area of small lakes was 024times 106 km2 rep-

resenting 17 of the total lake area The combined 95 uncertainty for the three classes ranged between 015 and038times 106 km2 (Table 3 Fig S8) suggesting that smalllakes represent between 11 and 26 of the total lake areaPrevious assessments have estimated that open-water ecosys-tems lt 01 km2 represent between 21 and 31 of global lakearea (Holgerson and Raymond 2016) but relied on assump-tions in the statistical modelling which may lead to bias forboreal and arctic regions (Cael and Seekell 2016 Muster etal 2019)

33 Rivers

Rivers were predicted to cover a total of 012times 106 km2 or047 of the BAWLD domain Large Rivers accounted for

Figure 5 Predicted distributions of lake and river classes within the BAWLD domain (a) Large Lakes (b) Midsize Glacial Lakes (c) Mid-size Peatland Lakes (d) Midsize Yedoma Lakes (e) Small Glacial Lakes (f) Small Peatland Lakes (g) Small Yedoma Lakes (h) LargeRivers (i) Small Organic-Rich Rivers (j) Small Organic-Poor Rivers

65 of the total river area in BAWLD These estimates weresimilar to global assessments where streams and rivers havebeen estimated to cover between 030 and 056 of theland area with 65 of the river area consisting of large riversof sixth or greater stream order (Downing et al 2012) Thepredictive model for Large Rivers was strongly influenced by

the area of ldquolarge riversrdquo in GLWD but experts consistentlymade lower assessments which led to an overall 15 lowerarea of Large Rivers compared to the area of rivers in GLWDwithin the BAWLD domain

Small Organic-Poor and Small Organic-Rich Rivers wereestimated to represent 27 and 8 respectively of the to-

tal river area The predictive models for the Small Organic-Poor and Organic-Rich Rivers explained 19 and 59 of the expert assessments and were distinctly influencedby the area of ldquooccasional inundationrdquo in GSW and ldquowet-landsrdquo in GLC30 respectively The estimated area of smallrivers varied among experts reflecting difficulties in consis-tent assessments among experts for land cover classes withlow extents (lt 1 in most grid cells) The distributions ofexpert assessments for small river areas were non-normalleading to a long upper tail for the 95 confidence inter-val (Fig S9) For example the low central and high esti-mates for the area of Small Organic-Rich Rivers were 0005010 and 054times 106 km2 respectively The predicted distri-butions showed that the Small Organic-Rich Rivers class wasclosely associated with the distribution of the BAWLD wet-land classes while Small Organic-Poor Rivers dominatedelsewhere with especially high abundances in regions withhigher mean annual precipitation (Fig 5)

34 Other classes

Boreal Forest Dry Tundra Rocklands and Glaciers werepredicted to cover 107 53 27 and 21times 106 km2 respec-tively within the BAWLD domain (Fig S10) The predictivemodels explained between 96 (Glaciers) and 67 (Rock-lands) of the variability in expert assessments While the pre-dictive models for Glaciers were almost exclusively influ-enced by the area of ldquopermanent snow and icerdquo in GL30several variables influenced predictions of Rocklands ndash in-cluding area of ldquorocklandsrdquo in NCS ldquomountainousrdquo andldquoruggedrdquo terrain in PZI and ldquobarrensrdquo in CAVM The pre-dictive models for Boreal Forest and Tundra suggested thatthe transition between these classes was strongly influencedby the area ldquoforestrdquo in GLC2 and by the distinction betweenldquotundrardquo and ldquoborealrdquo terrestrial ecoregions in TEW

35 Wetscapes

We defined ldquowetscapesrdquo as regions with characteristic com-position of specific wetland lake and river classes Our clus-tering analysis distinguished 15 typical wetscapes within theBAWLD domain (Fig 6) each defined by the relative pres-ence or absence of the 19 BAWLD classes (Table S1) Visu-alizing the distribution of wetscapes provides information onregions that are likely to have similarities in the magnitudeseasonality and climatic controls over CH4 emissions

Three wetscapes common in boreal regions were differ-entiated based on the abundance of non-permafrost wet-lands The Sparse Common and Dominant Boreal Wet-lands wetscapes all had limited lake coverage (lt 6 onaverage) but had 15 35 and 60 combined cover-ages of Bogs Fens and Marshes respectively The Domi-nant Boreal Wetlands wetscape was almost exclusive to thenon-permafrost regions of the Hudson Bay Lowlands andthe West Siberian Lowlands The Common Boreal Wetlands

wetscape was more widespread found adjacent to the coreareas of the Hudson Bay Lowlands and the West SiberianLowlands but also in the Mackenzie River Basin northernFinland European Russia and in the Kamchatka LowlandsThe Sparse Boreal Wetlands wetscape was widespread inSweden Finland European Russia and the southern borealregions of Canada outside of Yukon Emissions of CH4 fromthese regions are likely dominated by wetlands rather thanlakes with main sensitivity to climate change being alteredwater balance (Tarnocai 2006 Olefeldt et al 2017 Olson etal 2013)

The Lake-Rich Peatlands and the Permafrost Peatlandswetscapes were both found in lowland regions with discon-tinuous permafrost near the boreal-to-tundra transition TheLake-Rich Peatlands wetscape was almost exclusively foundin the West Siberian Lowlands north of the Ob River Thiswetscape was characterized by roughly equal abundances ofBogs Fens and Permafrost Bogs (each 14 ndash16 ) alongwith 8 Marshes 9 Small Peatland Lakes and 5 Mid-size Peatland Lakes It is notable that this wetscape with thehighest coverages of high-CH4-emitting marshes and peat-land lakes has no presence in North America The Per-mafrost Peatlands wetscape was conversely primarily foundin the Hudson Bay Lowlands and the Mackenzie River Basinwith additional coverage along the Arctic Ocean coast in Eu-ropean Russia in interior Alaska and in the Anadyr Low-lands of far-eastern Russia This wetscape had the greatestabundance of Permafrost Bogs (27 ) with less contribu-tion from other wetland classes (16 ) and relatively lowabundance of lakes (7 ) The Lake-Rich Peatlands wetscapelikely has the highest regional CH4 emissions while the Per-mafrost Peatlands wetscape likely has low to moderate emis-sions However CH4 emissions from both these wetscapesare likely highly sensitive to climate change due to the rapidongoing and future permafrost thaw that causes expansionof thermokarst lakes and non-permafrost wetlands at the ex-pense of Permafrost Bogs (Baumlckstrand et al 2008 Turetskyet al 2002)

Three wetscapes were found in lowland tundra regionsand varied in relative dominance of different wetland andlake classes Wetland-Rich Tundra had 23 wetlands butonly 7 lakes and was found on the Gydan and Taymyrpeninsulas in northern Siberia with minor extents in far-eastern Siberia and in Alaska Wetland- and Lake-Rich Tun-dra had similar wetland cover (24 ) but twice the cover-age of lakes (15 ) split equally between glacial and peat-land lakes It was found on the Alaska North Slope alongwith minor extents on the Yamal Peninsula the MackenzieRiver Delta and on sections of Baffin Island Lastly theWetland- and Lake-Rich Yedoma Tundra was characterizedby the highest abundance of yedoma lakes (8 ) and a totalwetland and lake coverage of 46 and was primarily foundin the Kolyma Lowlands with minor extents in the YukonndashKuskokwim Delta and on the Alaska North Slope These re-gions may have sensitive CH4 emissions particularly asso-

Figure 6 Wetscapes of the BorealndashArctic Wetland and Lake Dataset Wetscapes are defined by their characteristic composition of theBAWLD land cover classes and thus group regions with similar abundances (or absences) of specific wetland lake and river classes The 15wetscapes have their average land cover composition indicated by pie charts with the legend shown in the bottom left For clarity the smalland midsized lake classes were combined for glacial peatland and yedoma lakes and the river classes were omitted from the pie charts Noland cover pie charts are shown for the Large Lakes Rivers and Glaciers wetscapes

ciated with thermokarst lake expansion where highly labileyedoma sediments fuel high CH4 production (Walter An-thony et al 2016)

The remaining seven wetscapes are likely to have overalllow CH4 emissions or even net uptake resulting from ei-ther the dominance of low-CH4-emitting classes or the rel-ative absence of wetland and lake classes The Dry Tun-dra wetscape was common in regions of undulating topog-raphy of northernmost Siberia the Alaska North Slope andthe western Canadian arctic and was characterized by rel-atively low abundances of wetlands (9 ) and lakes (3 )

The Lake-Rich Shield wetscape was exclusive to the Cana-dian Shield and although it had a high abundance of lakes(18 ) these were almost completely dominated by low-CH4-emitting large lakes and glacial lakes The Upland Bo-real wetscape dominates boreal regions of Siberia but is alsofound in the Yukon Alaska and Quebec and was defined byhaving lt 5 wetlands and 05 lakes The Alpine and Tun-dra Barrens wetscape had lt 2 wetlands andsim 15 lakesand dominates the Greenland coast the high-latitude polardeserts of the Canadian Arctic Archipelago and the moun-tain ranges in Fennoscandia Alaska Yukon and eastern

Siberia Lastly the Glaciers Large Lakes and Large Riverswetscapes were defined by the dominance of the namesakeBAWLD classes

4 Data availability

The fractional land cover estimates from the BorealndashArcticWetland and Lake Dataset (BAWLD) are freely avail-able at the Arctic Data Center (Olefeldt et al 2021)httpsdoiorg1018739A2C824F9X The dataset is pro-vided as an ESRI shapefile (shp) and as a Keyhole MarkupLanguage (kml) file

5 Conclusions

The BorealndashArctic Wetland and Lake Dataset (BAWLD) wasdeveloped to provide improved estimates of areal extentsof five wetland classes seven lentic ecosystem classes andthree lotic ecosystem classes by leveraging expert knowledgealong with available spatial data By differentiating betweenwetland lake and river classes with distinct characteristicsBAWLD will be suitable to support large-scale modellingof high-latitude hydrological and biogeochemical impacts ofclimate change In particular BAWLD has been developedwith the aim to facilitate improved modelling of current andfuture CH4 emissions For example a companion dataset ofempirical CH4 data (BAWLD-CH4) (Kuhn et al 2021) wasco-developed with BAWLD ensuring that the land coverclassification was meaningful for the separation of classesbased on distinct magnitudes and controls of CH4 emissionsFuture assessments of borealndasharctic CH4 emissions based oncombined use of the BAWLD and BAWLD-CH4 datasetswill thus provide several refinements compared to previousbottom-up estimates In the future higher-spatial-resolutioncircumpolar wetland maps could be produced with machinelearning models and predictors calculated from multiple re-mote sensing data sources such as Sentinel-1 synthetic aper-ture radar (SAR) optical Sentinel-2 and Landsat 8 and Arc-ticDEM topographic data However the production of suchmaps would require spatially extensive field inventory datathorough expert assessment or accurate local wetland mapsas training and validation data By being based on expertassessment and an existing spatial dataset rather than a re-mote sensing approach BAWLD was able to provide predic-tions for abundance of high-CH4-emitting wetland and lakeclasses that have limited extents but disproportionate influ-ences on regional and overall CH4 emission (ie accountfor landscape CH4 hotspots) Using BAWLD for upscalingof CH4 emissions will reduce issues of representativeness ofempirical data for upscaling reduce the risk of overlap be-tween wetland and lake classes and allow for more rigorousuncertainty analysis

Supplement The supplement related to this article is availableonline at httpsdoiorg105194essd-13-5127-2021-supplement

Author contributions This study was conceived by DO TheGIS work was done by MH The information sent to experts to com-plete the expert assessment was compiled by DO MH and MAKAll co-authors completed the expert assessment The random for-est modelling was led by DO with input from TB AR and MJLData analysis and visualizations were led by DO with input from allco-authors The manuscript was written by DO with contributionsfrom all co-authors

Competing interests The contact author has declared that nei-ther they nor their co-authors have any competing interests

Disclaimer Publisherrsquos note Copernicus Publications remainsneutral with regard to jurisdictional claims in published maps andinstitutional affiliations

Acknowledgements This project was supported by the Per-mafrost Carbon Network

Financial support Financial support to David Olefeldt was pro-vided the National Science and Engineering Research Council ofCanada (NSERC) Discovery grant (RGPIN-2016-04688) and theCampus Alberta Innovates Program Claire Treat was supportedby the ERC (no851181) and the Helmholtz Impulse and Net-working Fund Avni Malhotra was supported by the Gordon andBetty Moore Foundation (grant GBMF5439 839 Stanford Uni-versity) David Bastviken was supported by the ERC (no725546)the Swedish Research Council VR (no2016-04829) and FOR-MAS (no2018-01794) Frans-Jan W Parmentier was supported bythe Norwegian Research Council under grant agreement 274711and the Swedish Research Council under registration no 2017-05268 Guido Grosse was supported through the BMBF KoPf Syn-thesis project (03F0834B) Jennifer D Watts was supported byNASA Earth Science (NNH17ZDA001N) Mark J Lara was sup-ported by NSF-EnvE (no1928048) Maria Strack was supportedby the Natural Sciences and Engineering Research Council ofCanada (NSERC) through the Canada Research Chairs programRuth K Varner was supported by the National Aeronautics andSpace Administration IDS program (NASA grant NNX17AK10G)Sarah A Finkelstein was supported by the Natural Sciences andEngineering Research Council of Canada Suzanne E Tank wassupported by funding from the Campus Alberta Innovates Pro-gram Ducks Unlimited Canadarsquos wetland inventories were fundedby various partnering organizations Environment and ClimateChange Canada Canadian Space Agency Government of AlbertaGovernment of Saskatchewan US Forest Service US Fish andWildlife Service PEW Charitable Trusts Canadian Boreal Ini-tiative Alberta-Pacific Forest Industries Inc Mistik ManagementLtd Louisiana-Pacific Forest Products Association of CanadaWeyerhaeuser Lakeland Industry and Community Encana Impe-rial Oil Devon Energy Corporation Shell Canada Energy Sun-

cor Foundation Treaty 8 Tribal Corporation (ldquoAkaitchordquo) and De-hcho First Nations The Permafrost Carbon Network provided co-ordination support and is funded by the NSF PLR Arctic SystemScience Research Networking Activities (RNA) Permafrost CarbonNetwork Synthesizing Flux Observations for Benchmarking ModelProjections of Permafrost Carbon Exchange (grant no 1931333(2019ndash2023))

Review statement This paper was edited by David Carlson andreviewed by three anonymous referees

References

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Hydrol Earth Syst Sci 25 17ndash40 httpsdoiorg105194hess-25-17-2021 2021

Bohn T J Melton J R Ito A Kleinen T Spahni R StockerB D Zhang B Zhu X Schroeder R Glagolev M VMaksyutov S Brovkin V Chen G Denisov S N EliseevA V Gallego-Sala A McDonald K C Rawlins M A Ri-ley W J Subin Z M Tian H Zhuang Q and Kaplan J OWETCHIMP-WSL intercomparison of wetland methane emis-sions models over West Siberia Biogeosciences 12 3321ndash3349httpsdoiorg105194bg-12-3321-2015 2015

Bridgham S D Cadillo-Quiroz H Keller J K andZhuang Q Methane emissions from wetlands biogeo-chemical microbial and modeling perspectives from lo-cal to global scales Glob Change Biol 19 1325ndash1346httpsdoiorg101111gcb12131 2013

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Bruhwiler L Parmentier F-J W Crill P Leonard M andPalmer P I The Arctic Carbon Cycle and Its Responseto Changing Climate Curr Clim Change Rep 7 14ndash34httpsdoiorg101007s40641-020-00169-5 2021

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Bubier J L Moore T R Bellisario L Comer N T andCrill P M Ecological controls on methane emissions from aNorthern Peatland Complex in the zone of discontinuous per-mafrost Manitoba Canada Global Biogeochem Cy 9 455ndash470 httpsdoiorg10102995GB02379 1995

Buumlttner G CORINE Land Cover and Land Cover Change Prod-ucts in Land Use and Land Cover Mapping in Europe Prac-tices amp Trends edited by Manakos I and Braun M SpringerNetherlands Dordrecht 55ndash74 httpsdoiorg101007978-94-007-7969-3_5 2014

Cael B B and Seekell D A The size-distribution of Earthrsquos lakesSci Rep-UK 6 29633 httpsdoiorg101038srep296332016

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Chasmer L Mahoney C Millard K Nelson K Peters DMerchant M Hopkinson C Brisco B Niemann O Mont-gomery J Devito K and Cobbaert D Remote Sensing ofBoreal Wetlands 2 Methods for Evaluating Boreal WetlandEcosystem State and Drivers of Change Remote Sens 12 1321httpsdoiorg103390rs12081321 2020

Chen J Chen J Liao A Cao X Chen L Chen X HeC Han G Peng S Lu M Zhang W Tong X and MillsJ Global land cover mapping at 30 m resolution A POK-based operational approach ISPRS J Photogramm 103 7ndash27httpsdoiorg101016jisprsjprs201409002 2015

Chen Y Hu F S and Lara M J Divergent shrub-cover re-sponses driven by climate wildfire and permafrost interactionsin Arctic tundra ecosystems Glob Change Biol 27 652ndash663httpsdoiorg101111gcb15451 2021

Cooley S W Smith L C Stepan L and Mascaro J Track-ing Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery Remote Sens 9 1306httpsdoiorg103390rs9121306 2017

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Fluet-Chouinard E Lehner B Rebelo L-M Papa F andHamilton S K Development of a global inundation map athigh spatial resolution from topographic downscaling of coarse-scale remote sensing data Remote Sens Environ 158 348ndash361httpsdoiorg101016jrse201410015 2015

Glagolev M Kleptsova I Filippov I Maksyutov Sand Machida T Regional methane emission from WestSiberia mire landscapes Environ Res Lett 6 045214httpsdoiorg1010881748-932664045214 2011

Glaser P H Siegel D I Reeve A S Janssens J Aand Janecky D R Tectonic drivers for vegetation pat-terning and landscape evolution in the Albany River re-gion of the Hudson Bay Lowlands J Ecol 92 1054ndash1070httpsdoiorg101111j0022-0477200400930x 2004

Grosse G Jones B and Arp C 821 Thermokarst LakesDrainage and Drained Basins in Treatise on Geomorphologyedited by Shroder J F Academic Press San Diego 325ndash353httpsdoiorg101016B978-0-12-374739-600216-5 2013

Gruber S Derivation and analysis of a high-resolution estimateof global permafrost zonation The Cryosphere 6 221ndash233httpsdoiorg105194tc-6-221-2012 2012

Gunnarsson U Loumlfroth M and Sandring S The Swedish wet-land survey compiled excerpts from the national final reportSwedish Environmental Protection Agency Stockholm 37 pp2014

Heikkinen J E P Virtanen T Huttunen J T ElsakovV and Martikainen P J Carbon balance in East Eu-ropean tundra Global Biogeochem Cy 18 GB1023httpsdoiorg1010292003GB002054 2004

Heiskanen L Tuovinen J-P Raumlsaumlnen A Virtanen T JuutinenS Lohila A Penttilauml T Linkosalmi M Mikola J LaurilaT and Aurela M Carbon dioxide and methane exchange ofa patterned subarctic fen during two contrasting growing sea-sons Biogeosciences 18 873ndash896 httpsdoiorg105194bg-18-873-2021 2021

Helbig M Pappas C and Sonnentag O Permafrost thaw andwildfire Equally important drivers of boreal tree cover changesin the Taiga Plains Canada Geophys Res Lett 43 1598ndash1606httpsdoiorg1010022015GL067193 2016

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Holgerson M A and Raymond P A Large contribution to in-land water CO2 and CH4 emissions from very small ponds NatGeosci 9 222ndash226 httpsdoiorg101038ngeo2654 2016

Homer C Dewitz J Jin S Xian G Costello C Daniel-son P Gass L Funk M Wickham J Stehman SAuch R and Riitters K Conterminous United States landcover change patterns 2001ndash2016 from the 2016 NationalLand Cover Database ISPRS J Photogramm 162 184ndash199httpsdoiorg101016jisprsjprs202002019 2020

Hugelius G Tarnocai C Broll G Canadell J G Kuhry Pand Swanson D K The Northern Circumpolar Soil CarbonDatabase spatially distributed datasets of soil coverage and soilcarbon storage in the northern permafrost regions Earth SystSci Data 5 3ndash13 httpsdoiorg105194essd-5-3-2013 2013

Hugelius G Strauss J Zubrzycki S Harden J W Schuur EA G Ping C-L Schirrmeister L Grosse G Michaelson GJ Koven C D OrsquoDonnell J A Elberling B Mishra UCamill P Yu Z Palmtag J and Kuhry P Estimated stocksof circumpolar permafrost carbon with quantified uncertaintyranges and identified data gaps Biogeosciences 11 6573ndash6593httpsdoiorg105194bg-11-6573-2014 2014

Hugelius G Loisel J Chadburn S Jackson R B Jones MMacDonald G Marushchak M Olefeldt D Packalen MSiewert M B Treat C Turetsky M Voigt C and Yu ZLarge stocks of peatland carbon and nitrogen are vulnerable topermafrost thaw P Natl Acad Sci USA 117 20438ndash20446httpsdoiorg101073pnas1916387117 2020

Ito A Methane emission from pan-Arctic natural wetlands esti-mated using a process-based model 1901ndash2016 Polar Sci 2126ndash36 httpsdoiorg101016jpolar201812001 2019

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with a Warming Climate in Central Alaska Climatic Change 48551ndash579 httpsdoiorg101023A1005667424292 2001

Juncher Joslashrgensen C Lund Johansen K M Westergaard-NielsenA and Elberling B Net regional methane sink in HighArctic soils of northeast Greenland Nat Geosci 8 20ndash23httpsdoiorg101038ngeo2305 2015

Juutinen S Alm J Larmola T Huttunen J T Morero M Mar-tikainen P J and Silvola J Major implication of the littoralzone for methane release from boreal lakes Global BiogeochemCy 17 httpsdoiorg1010292003GB002105 2003

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Knoblauch C Spott O Evgrafova S Kutzbach L and PfeifferE-M Regulation of methane production oxidation and emis-sion by vascular plants and bryophytes in ponds of the northeastSiberian polygonal tundra J Geophys Res-Biogeo 120 2525ndash2541 httpsdoiorg1010022015JG003053 2015

Knox S H Jackson R B Poulter B McNicol G Fluet-Chouinard E Zhang Z Hugelius G Bousquet P CanadellJ G Saunois M Papale D Chu H Keenan T F Baldoc-chi D Torn M S Mammarella I Trotta C Aurela MBohrer G Campbell D I Cescatti A Chamberlain S ChenJ Chen W Dengel S Desai A R Euskirchen E FriborgT Gasbarra D Goded I Goeckede M Heimann M Hel-big M Hirano T Hollinger D Y Iwata H Kang M KlattJ Krauss K W Kutzbach L Lohila A Mitra B Morin TH Nilsson M B Niu S Noormets A Oechel W C PeichlM Peltola O Reba M L Richardson A D Runkle B RK Ryu Y Sachs T Schaumlfer K V R Schmid H P Shur-pali N Sonnentag O Tang A C I Ueyama M Vargas RVesala T Ward E J Windham-Myers L Wohlfahrt G andZona D FLUXNET-CH4 Synthesis Activity Objectives Ob-servations and Future Directions B Am Meteorol Soc 1002607ndash2632 httpsdoiorg101175BAMS-D-18-02681 2019

Kremenetski K V Velichko A A Borisova O K MacDon-ald G M Smith L C Frey K E and Orlova L A Peat-lands of the Western Siberian lowlands current knowledge onzonation carbon content and Late Quaternary history Qua-ternary Sci Rev 22 703ndash723 httpsdoiorg101016S0277-3791(02)00196-8 2003

Kuhn M caret Classification and Regression Training R pack-age version 60-86 available at httpsCRANR-projectorgpackage=caret (last access 31 October 2021) 2020

Kuhn M A Varner R K Bastviken D Crill P MacIntyre STuretsky M Walter Anthony K McGuire A D and OlefeldtD BAWLD-CH4 a comprehensive dataset of methane fluxesfrom boreal and arctic ecosystems Earth Syst Sci Data 135151ndash5189 httpsdoiorg105194essd-13-5151-2021 2021

Lara M J and Chipman M L Periglacial Lake Origin Influencesthe Likelihood of Lake Drainage in Northern Alaska RemoteSens 13 853 httpsdoiorg103390rs13050852 2021

Lara M J Nitze I Grosse G and McGuire A D Tundralandform and vegetation productivity trend maps for the Arc-tic Coastal Plain of northern Alaska Sci Rep 5 180058httpsdoiorg101038sdata201858 2018

Lau M C Y Stackhouse B T Layton A C Chauhan A Vish-nivetskaya T A Chourey K Ronholm J Mykytczuk N C

S Bennett P C Lamarche-Gagnon G Burton N PollardW H Omelon C R Medvigy D M Hettich R L PfiffnerS M Whyte L G and Onstott T C An active atmosphericmethane sink in high Arctic mineral cryosols ISME J 9 1880ndash1891 httpsdoiorg101038ismej201513 2015

Lehner B and Doumlll P Development and validation of a globaldatabase of lakes reservoirs and wetlands J Hydrol 296 1ndash22httpsdoiorg101016jjhydrol200403028 2004

Li M Peng C Zhu Q Zhou X Yang G Song X andZhang K The significant contribution of lake depth in regu-lating global lake diffusive methane emissions Water Res 172115465 httpsdoiorg101016jwatres2020115465 2020

Liaw A and Wiener M Classification and Regression by random-Forest R News 2 18ndash22 2002

Liljedahl A K Boike J Daanen R P Fedorov A N FrostG V Grosse G Hinzman L D Iijma Y Jorgenson J CMatveyeva N Necsoiu M Raynolds M K Romanovsky VE Schulla J Tape K D Walker D A Wilson C J YabukiH and Zona D Pan-Arctic ice-wedge degradation in warmingpermafrost and its influence on tundra hydrology Nat Geosci9 312ndash318 httpsdoiorg101038ngeo2674 2016

Linke S Lehner B Ouellet Dallaire C Ariwi J Grill GAnand M Beames P Burchard-Levine V Maxwell SMoidu H Tan F and Thieme M Global hydro-environmentalsub-basin and river reach characteristics at high spatial resolu-tion Sci Data 6 283 httpsdoiorg101038s41597-019-0300-6 2019

Loisel J Gallego-Sala A V Amesbury M J Magnan G An-shari G Beilman D W Benavides J C Blewett J CamillP Charman D J Chawchai S Hedgpeth A Kleinen T Ko-rhola A Large D Mansilla C A Muumlller J van Bellen SWest J B Yu Z Bubier J L Garneau M Moore T San-nel A B K Page S Vaumlliranta M Bechtold M BrovkinV Cole L E S Chanton J P Christensen T R DaviesM A De Vleeschouwer F Finkelstein S A Frolking SGałka M Gandois L Girkin N Harris L I HeinemeyerA Hoyt A M Jones M C Joos F Juutinen S Kaiser KLacourse T Lamentowicz M Larmola T Leifeld J LohilaA Milner A M Minkkinen K Moss P Naafs B D ANichols J OrsquoDonnell J Payne R Philben M Piilo S Quil-let A Ratnayake A S Roland T P Sjoumlgersten S SonnentagO Swindles G T Swinnen W Talbot J Treat C ValachA C and Wu J Expert assessment of future vulnerability ofthe global peatland carbon sink Nat Clim Change 11 70ndash77httpsdoiorg101038s41558-020-00944-0 2021

Machacova K Baumlck J Vanhatalo A Halmeenmaumlki E Ko-lari P Mammarella I Pumpanen J Acosta M Urban Oand Pihlatie M Pinus sylvestris as a missing source of nitrousoxide and methane in boreal forest Sci Rep-UK 6 23410httpsdoiorg101038srep23410 2016

Malhotra A and Roulet N T Environmental correlates ofpeatland carbon fluxes in a thawing landscape do transi-tional thaw stages matter Biogeosciences 12 3119ndash3130httpsdoiorg105194bg-12-3119-2015 2015

Marushchak M E Friborg T Biasi C Herbst M JohanssonT Kiepe I Liimatainen M Lind S E Martikainen P JVirtanen T Soegaard H and Shurpali N J Methane dynam-ics in the subarctic tundra combining stable isotope analyses

plot- and ecosystem-scale flux measurements Biogeosciences13 597ndash608 httpsdoiorg105194bg-13-597-2016 2016

Masing V Botch M and Laumlaumlnelaid A Mires of the for-mer Soviet Union Wetlands Ecol Manage 18 397ndash433httpsdoiorg101007s11273-008-9130-6 2010

Matson A Pennock D and Bedard-Haughn A Methane and ni-trous oxide emissions from mature forest stands in the borealforest Saskatchewan Canada Forest Ecol Manage 258 1073ndash1083 httpsdoiorg101016jforeco200905034 2009

Matthews E and Fung I Methane emission from naturalwetlands Global distribution area and environmental char-acteristics of sources Global Biogeochem Cy 1 61ndash86httpsdoiorg101029GB001i001p00061 1987

McGuire A D Christensen T R Hayes D Heroult A Eu-skirchen E Kimball J S Koven C Lafleur P Miller PA Oechel W Peylin P Williams M and Yi Y An assess-ment of the carbon balance of Arctic tundra comparisons amongobservations process models and atmospheric inversions Bio-geosciences 9 3185ndash3204 httpsdoiorg105194bg-9-3185-2012 2012

Melton J R Wania R Hodson E L Poulter B Ringeval BSpahni R Bohn T Avis C A Beerling D J Chen GEliseev A V Denisov S N Hopcroft P O Lettenmaier DP Riley W J Singarayer J S Subin Z M Tian H ZuumlrcherS Brovkin V van Bodegom P M Kleinen T Yu Z Cand Kaplan J O Present state of global wetland extent andwetland methane modelling conclusions from a model inter-comparison project (WETCHIMP) Biogeosciences 10 753ndash788 httpsdoiorg105194bg-10-753-2013 2013

Messager M L Lehner B Grill G Nedeva I and SchmittO Estimating the volume and age of water stored in globallakes using a geo-statistical approach Nat Commun 7 13603httpsdoiorg101038ncomms13603 2016

Kursa M B and Rudnicki W R Feature Selection with the BorutaPackage J Stat Softw 36 1ndash13 httpwwwjstatsoftorgv36i11 (last access 31 October 2021) 2010

Muster S Roth K Langer M Lange S Cresto Aleina FBartsch A Morgenstern A Grosse G Jones B Sannel AB K Sjoumlberg Y Guumlnther F Andresen C Veremeeva ALindgren P R Bouchard F Lara M J Fortier D Charbon-neau S Virtanen T A Hugelius G Palmtag J Siewert MB Riley W J Koven C D and Boike J PeRL a circum-Arctic Permafrost Region Pond and Lake database Earth SystSci Data 9 317ndash348 httpsdoiorg105194essd-9-317-20172017

Muster S Riley W J Roth K Langer M Cresto AleinaF Koven C D Lange S Bartsch A Grosse G Wil-son C J Jones B M and Boike J Size Distributions ofArctic Waterbodies Reveal Consistent Relations in Their Sta-tistical Moments in Space and Time Front Earth Sci 7 5httpsdoiorg103389feart201900005 2019

Olefeldt D Turetsky M R Crill P M and McGuire AD Environmental and physical controls on northern terrestrialmethane emissions across permafrost zones Glob Change Biol19 589ndash603 httpsdoiorg101111gcb12071 2013

Olefeldt D Goswami S Grosse G Hayes D Hugelius GKuhry P McGuire A D Romanovsky V E Sannel A B KSchuur E A G and Turetsky M R Circumpolar distribution

and carbon storage of thermokarst landscapes Nat Commun 713043 httpsdoiorg101038ncomms13043 2016

Olefeldt D Euskirchen E S Harden J Kane E McGuire AD Waldrop M P and Turetsky M R A decade of boreal richfen greenhouse gas fluxes in response to natural and experimen-tal water table variability Glob Change Biol 23 2428ndash2440httpsdoiorg101111gcb13612 2017

Olefeldt D Hovemyr M Kuhn M A Bastviken D Bohn T JConnolly J Crill P Euskirchen E S Finkelstein S A GenetH Grosse G Harris L I Heffernan L Helbig M HugeliusG Hutchins R Juutinen S Lara M J Malhotra A ManiesK McGuire A D Natali S M OrsquoDonnell J A ParmentierF-J W Raumlsaumlnen A Schaumldel C Sonnentag O Strack MTank S E Treat C Varner R K Virtanen T Warren R Kand Watts J D The fractional land cover estimates from theBoreal-Arctic Wetland and Lake Dataset (BAWLD) Arctic DataCenter httpsdoiorg1018739A2C824F9X 2021

Olson D M Dinerstein E Wikramanayake E D Burgess ND Powell G V N Underwood E C Drsquoamico J A ItouaI Strand H E Morrison J C Loucks C J Allnutt T FRicketts T H Kura Y Lamoreux J F Wettengel W WHedao P and Kassem K R Terrestrial Ecoregions of theWorld A New Map of Life on Earth A new global map of terres-trial ecoregions provides an innovative tool for conserving biodi-versity BioScience 51 933ndash938 httpsdoiorg1016410006-3568(2001)051[0933TEOTWA]20CO2 2001

Olson D M Griffis T J Noormets A Kolka R and Chen JInterannual seasonal and retrospective analysis of the methaneand carbon dioxide budgets of a temperate peatland J GeophysRes-Biogeo 118 226ndash238 httpsdoiorg101002jgrg200312013

Packalen M S Finkelstein S A and McLaughlin JW Climate and peat type in relation to spatial variationof the peatland carbon mass in the Hudson Bay Low-lands Canada J Geophys Res-Biogeo 121 1104ndash1117httpsdoiorg1010022015JG002938 2016

Pekel J-F Cottam A Gorelick N and Belward AS High-resolution mapping of global surface wa-ter and its long-term changes Nature 540 418ndash422httpsdoiorg101038nature20584 2016

Pelletier L Moore T R Roulet N T Garneau Mand Beaulieu-Audy V Methane fluxes from three peat-lands in the La Grande Riviegravere watershed James Baylowland Canada J Geophys Res-Biogeo 112 G01018httpsdoiorg1010292006JG000216 2007

Peltola O Vesala T Gao Y Raumlty O Alekseychik P AurelaM Chojnicki B Desai A R Dolman A J Euskirchen E SFriborg T Goumlckede M Helbig M Humphreys E JacksonR B Jocher G Joos F Klatt J Knox S H Kowalska NKutzbach L Lienert S Lohila A Mammarella I Nadeau DF Nilsson M B Oechel W C Peichl M Pypker T Quin-ton W Rinne J Sachs T Samson M Schmid H P Son-nentag O Wille C Zona D and Aalto T Monthly griddeddata product of northern wetland methane emissions based on up-scaling eddy covariance observations Earth Syst Sci Data 111263ndash1289 httpsdoiorg105194essd-11-1263-2019 2019

Raumlsaumlnen A and Virtanen T Data and resolution re-quirements in mapping vegetation in spatially heteroge-

neous landscapes Remote Sens Environ 230 111207httpsdoiorg101016jrse201905026 2019

Raynolds M K Walker D A Balser A Bay C Campbell MCherosov M M Danieumlls F J A Eidesen P B ErmokhinaK A Frost G V Jedrzejek B Jorgenson M T Kennedy BE Kholod S S Lavrinenko I A Lavrinenko O V Mag-nuacutesson B Matveyeva N V Metuacutesalemsson S Nilsen LOlthof I Pospelov I N Pospelova E B Pouliot D Raz-zhivin V Schaepman-Strub G Šibiacutek J Telyatnikov M Yuand Troeva E A raster version of the Circumpolar Arctic Veg-etation Map (CAVM) Remote Sens Environ 232 111297httpsdoiorg101016jrse2019111297 2019

R Core Team R A language and environment for statistical com-puting R Foundation for Statistical Computing Vienna Austriaavailable at httpswwwR-projectorg (last access 31 Octo-ber 2021) 2020

Rubec C The Canadian Wetland Classification System in TheWetland Book I Structure and Function Management andMethods edited by Finlayson C M Everard M IrvineK McInnes R J Middleton B A van Dam A A andDavidson N C Springer Netherlands Dordrecht 1577ndash1581httpsdoiorg101007978-90-481-9659-3_340 2018

Saunois M Stavert A R Poulter B Bousquet P Canadell JG Jackson R B Raymond P A Dlugokencky E J Houwel-ing S Patra P K Ciais P Arora V K Bastviken D Berga-maschi P Blake D R Brailsford G Bruhwiler L Carl-son K M Carrol M Castaldi S Chandra N Crevoisier CCrill P M Covey K Curry C L Etiope G FrankenbergC Gedney N Hegglin M I Houmlglund-Isaksson L HugeliusG Ishizawa M Ito A Janssens-Maenhout G Jensen KM Joos F Kleinen T Krummel P B Langenfelds R LLaruelle G G Liu L Machida T Maksyutov S McDon-ald K C McNorton J Miller P A Melton J R MorinoI Muumlller J Murguia-Flores F Naik V Niwa Y Noce SOrsquoDoherty S Parker R J Peng C Peng S Peters G PPrigent C Prinn R Ramonet M Regnier P Riley W JRosentreter J A Segers A Simpson I J Shi H Smith SJ Steele L P Thornton B F Tian H Tohjima Y TubielloF N Tsuruta A Viovy N Voulgarakis A Weber T Svan Weele M van der Werf G R Weiss R F Worthy DWunch D Yin Y Yoshida Y Zhang W Zhang Z ZhaoY Zheng B Zhu Q Zhu Q and Zhuang Q The GlobalMethane Budget 2000ndash2017 Earth Syst Sci Data 12 1561ndash1623 httpsdoiorg105194essd-12-1561-2020 2020

Sayedi S S Abbott B W Thornton B F Frederick J M VonkJ E Overduin P Schaumldel C Schuur E A G BourbonnaisA Demidov N Gavrilov A He S Hugelius G Jakobs-son M Jones M C Joung D Kraev G Macdonald R WMcGuire A D Mu C OrsquoRegan M Schreiner K M StranneC Pizhankova E Vasiliev A Westermann S Zarnetske J PZhang T Ghandehari M Baeumler S Brown B C and FreiR J Subsea permafrost carbon stocks and climate change sen-sitivity estimated by expert assessment Environ Res Lett 15124075 httpsdoiorg1010881748-9326abcc29 2020

Schneider von Deimling T Grosse G Strauss J SchirrmeisterL Morgenstern A Schaphoff S Meinshausen M and BoikeJ Observation-based modelling of permafrost carbon fluxeswith accounting for deep carbon deposits and thermokarst activ-

ity Biogeosciences 12 3469ndash3488 httpsdoiorg105194bg-12-3469-2015 2015

Seppaumllauml M Synthesis of studies of palsa formation un-derlining the importance of local environmental andphysical characteristics Quaternary Res 75 366ndash370httpsdoiorg101016jyqres201009007 2011

Smith L C Sheng Y and MacDonald G M A first pan-Arcticassessment of the influence of glaciation permafrost topogra-phy and peatlands on northern hemisphere lake distribution Per-mafrost Periglac 18 201ndash208 httpsdoiorg101002ppp5812007

Song J Bias corrections for Random Forest in regression us-ing residual rotation J Korean Stat Soc 44 321ndash326httpsdoiorg101016jjkss201501003 2015

Stanley E H Casson N J Christel S T Crawford J T LokenL C and Oliver S K The ecology of methane in streams andrivers patterns controls and global significance Ecol Monogr86 146ndash171 httpsdoiorg10189015-1027 2016

St Pierre K A Danielsen B K Hermesdorf LDrsquoImperio L Iversen L L and Elberling B Driversof net methane uptake across Greenlandic dry heathtundra landscapes Soil Biol Biochem 138 107605httpsdoiorg101016jsoilbio2019107605 2019

Strauss J Schirrmeister L Grosse G Fortier D Hugelius GKnoblauch C Romanovsky V Schaumldel C Schneider vonDeimling T Schuur E A G Shmelev D Ulrich M andVeremeeva A Deep Yedoma permafrost A synthesis of depo-sitional characteristics and carbon vulnerability Earth-Sci Rev172 75ndash86 httpsdoiorg101016jearscirev2017070072017

Tan Z Zhuang Q Henze D K Frankenberg C DlugokenckyE Sweeney C Turner A J Sasakawa M and Machida TInverse modeling of pan-Arctic methane emissions at high spa-tial resolution what can we learn from assimilating satellite re-trievals and using different process-based wetland and lake bio-geochemical models Atmos Chem Phys 16 12649ndash12666httpsdoiorg105194acp-16-12649-2016 2016

Tarnocai C The effect of climate change on carbon inCanadian peatlands Global Planet Change 53 222ndash232httpsdoiorg101016jgloplacha200603012 2006

Terentieva I E Glagolev M V Lapshina E D SabrekovA F and Maksyutov S Mapping of West Siberiantaiga wetland complexes using Landsat imagery implica-tions for methane emissions Biogeosciences 13 4615ndash4626httpsdoiorg105194bg-13-4615-2016 2016

Terentieva I E Sabrekov A F Ilyasov D Ebrahimi AGlagolev M V and Maksyutov S Highly Dynamic MethaneEmission from the West Siberian Boreal Floodplains Wetlands39 217ndash226 httpsdoiorg101007s13157-018-1088-4 2019

Thompson R L Nisbet E G Pisso I Stohl A Blake D Dlu-gokencky E J Helmig D and White J W C Variability inAtmospheric Methane From Fossil Fuel and Microbial SourcesOver the Last Three Decades Geophys Res Lett 45 11499-11508 httpsdoiorg1010292018GL078127 2018

Thornton B F Wik M and Crill P M Double-counting challenges the accuracy of high-latitude methaneinventories Geophys Res Lett 43 12569ndash12577httpsdoiorg1010022016GL071772 2016

Treat C C Bloom A A and Marushchak M E Nongrowingseason methane emissions ndash a significant component of annualemissions across northern ecosystems Glob Change Biol 243331ndash3343 httpsdoiorg101111gcb14137 2018

Turetsky M R Wieder R K and Vitt D H Boreal peatland Cfluxes under varying permafrost regimes Soil Biol Biochem34 907ndash912 httpsdoiorg101016S0038-0717(02)00022-62002

Turetsky M R Kotowska A Bubier J Dise N B CrillP Hornibrook E R C Minkkinen K Moore T RMyers-Smith I H Nykaumlnen H Olefeldt D Rinne JSaarnio S Shurpali N Tuittila E-S Waddington J MWhite J R Wickland K P and Wilmking M A syn-thesis of methane emissions from 71 northern temperateand subtropical wetlands Glob Change Biol 20 2183ndash2197httpsdoiorg101111gcb12580 2014

Vaumlliranta M Salojaumlrvi N Vuorsalo A Juutinen S KorholaA Luoto M and Tuittila E-S Holocene fenndashbog transitionscurrent status in Finland and future perspectives Holocene 27752ndash764 httpsdoiorg1011770959683616670471 2017

van der Molen M K van Huissteden J Parmentier F J WPetrescu A M R Dolman A J Maximov T C KononovA V Karsanaev S V and Suzdalov D A The growing sea-son greenhouse gas balance of a continental tundra site in theIndigirka lowlands NE Siberia Biogeosciences 4 985ndash1003httpsdoiorg105194bg-4-985-2007 2007

Venter O Sanderson E W Magrach A Allan J R BeherJ Jones K R Possingham H P Laurance W F Wood PFekete B M Levy M A and Watson J E M Sixteen yearsof change in the global terrestrial human footprint and impli-cations for biodiversity conservation Nat Commun 7 12558httpsdoiorg101038ncomms12558 2016

Virtanen T and Ek M The fragmented nature of tundralandscape International Journal of Applied Earth Observa-tion and Geoinformation Int J Appl Earth Obs 27 4ndash12httpsdoiorg101016jjag201305010 2014

Vitt D H and Chee W-L The relationships of vege-tation to surface water chemistry and peat chemistryin fens of Alberta Canada Vegetatio 89 87ndash106httpsdoiorg101007BF00032163 1990

Vitt D H Halsey L A Bauer I E and Campbell C Spatialand temporal trends in carbon storage of peatlands of continentalwestern Canada through the Holocene Can J Earth Sci 37 12httpsdoiorg101139e99-097 2000a

Vitt D H Halsey L A and Zoltai S C The chang-ing landscape of Canadarsquos western boreal forest the currentdynamics of permafrost Can J Forest Res 30 283ndash287httpsdoiorg101139x99-214 2000b

Walker D A Raynolds M K Danieumlls F J A Einarsson EElvebakk A Gould W A Katenin A E Kholod S SMarkon C J Melnikov E S Moskalenko N G TalbotS S Yurtsev B A and the other members of the CAVMTeam The Circumpolar Arctic vegetation map 16 267ndash282httpsdoiorg101111j1654-11032005tb02365x 2005

Wallin M B Campeau A Audet J Bastviken D Bishop KKokic J Laudon H Lundin E Loumlfgren S Natchimuthu SSobek S Teutschbein C Weyhenmeyer G A and Grabs TCarbon dioxide and methane emissions of Swedish low-orderstreams ndash a national estimate and lessons learnt from more thana decade of observations Limnol Oceanogr-Lett 3 156ndash167httpsdoiorg101002lol210061 2018

Walter Anthony K Daanen R Anthony P Schneider vonDeimling T Ping C-L Chanton J P and Grosse GMethane emissions proportional to permafrost carbon thawedin Arctic lakes since the 1950s Nat Geosci 9 679ndash682httpsdoiorg101038ngeo2795 2016

Walter Anthony K Schneider von Deimling T Nitze I FrolkingS Emond A Daanen R Anthony P Lindgren P Jones Band Grosse G 21st-century modeled permafrost carbon emis-sions accelerated by abrupt thaw beneath lakes Nat Commun9 3262 httpsdoiorg101038s41467-018-05738-9 2018

Watts J D Kimball J S Bartsch A and McDonald K CSurface water inundation in the boreal-Arctic potential impactson regional methane emissions Environ Res Lett 9 075001httpsdoiorg1010881748-932697075001 2014

Whalen S C Reeburgh W S and Barber V A Ox-idation of methane in boreal forest soils a compari-son of seven measures Biogeochemistry 16 181ndash211httpsdoiorg101007BF00002818 1992

Wickland K P Jorgenson M T Koch J C Kanevskiy Mand Striegl R G Carbon Dioxide and Methane Flux in a Dy-namic Arctic Tundra Landscape Decadal-Scale Impacts of IceWedge Degradation and Stabilization Geophys Res Lett 47e2020GL089894 httpsdoiorg1010292020GL089894 2020

Wik M Varner R K Anthony K W MacIntyre S andBastviken D Climate-sensitive northern lakes and ponds arecritical components of methane release Nat Geosci 9 99ndash105httpsdoiorg101038ngeo2578 2016

Zhang Z Zimmermann N E Stenke A Li X HodsonE L Zhu G Huang C and Poulter B Emerging roleof wetland methane emissions in driving 21st century cli-mate change P Natl Acad Sci USA 114 9647ndash9652httpsdoiorg101073pnas1618765114 2017

Zhu Q Peng C Chen H Fang X Liu J Jiang H Yang Yand Yang G Estimating global natural wetland methane emis-sions using process modelling spatio-temporal patterns and con-tributions to atmospheric methane fluctuations Global Ecol Bio-geogr 24 959ndash972 httpsdoiorg101111geb12307 2015

Abstract
Introduction
Development of the BorealndashArctic Wetland and Lake Dataset
- Study domain and harmonization of available spatial data
- Land cover classes in BAWLD
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
  - - Expert assessment
    - Random forest model and uncertainty analysis
    - Evaluation against regional wetland datasets
    - - Results and discussion
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        Author contributions
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 2: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

Received 24 April 2021 ndash Discussion started 7 May 2021Revised 21 September 2021 ndash Accepted 4 October 2021 ndash Published 5 November 2021

Abstract Methane emissions from boreal and arctic wetlands lakes and rivers are expected to increase inresponse to warming and associated permafrost thaw However the lack of appropriate land cover datasets forscaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for ourunderstanding of present-day and future methane emissions Here we present the BorealndashArctic Wetland andLake Dataset (BAWLD) a land cover dataset based on an expert assessment extrapolated using random forestmodelling from available spatial datasets of climate topography soils permafrost conditions vegetation wet-lands and surface water extents and dynamics In BAWLD we estimate the fractional coverage of five wetlandseven lake and three river classes within 05times 05 grid cells that cover the northern boreal and tundra biomes(17 of the global land surface) Land cover classes were defined using criteria that ensured distinct methaneemissions among classes as indicated by a co-developed comprehensive dataset of methane flux observationsIn BAWLD wetlands occupied 32times 106 km2 (14 of domain) with a 95 confidence interval between 28and 38times 106 km2 Bog fen and permafrost bog were the most abundant wetland classes covering sim 28 each of the total wetland area while the highest-methane-emitting marsh and tundra wetland classes occupied5 and 12 respectively Lakes defined to include all lentic open-water ecosystems regardless of size cov-ered 14times 106 km2 (6 of domain) Low-methane-emitting large lakes (gt 10 km2) and glacial lakes jointlyrepresented 78 of the total lake area while high-emitting peatland and yedoma lakes covered 18 and 4 respectively Small (lt 01 km2) glacial peatland and yedoma lakes combined covered 17 of the total lake areabut contributed disproportionally to the overall spatial uncertainty in lake area with a 95 confidence intervalbetween 015 and 038times 106 km2 Rivers and streams were estimated to cover 012 times 106 km2 (05 of do-main) of which 8 was associated with high-methane-emitting headwaters that drain organic-rich landscapesDistinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLDdomain allowing for the mapping of ldquowetscapesrdquo that have characteristic methane emission magnitudes andsensitivities to climate change at regional scales With BAWLD we provide a dataset which avoids double-accounting of wetland lake and river extents and which includes confidence intervals for each land cover classAs such BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling effortsfor the northern boreal and arctic region in particular those aimed at improving assessments of current and futuremethane emissions Data are freely available at httpsdoiorg1018739A2C824F9X (Olefeldt et al 2021)

1 Introduction

Emissions of methane (CH4) from abundant wetlands lakesand rivers located in boreal and arctic regions are expectedto substantially increase this century due to rapid climatewarming and associated permafrost thaw (Walter Anthonyet al 2018 Ito 2019 Hugelius et al 2020 Schneider vonDeimling et al 2015 Zhang et al 2017) However pre-dicting future CH4 emissions is highly uncertain as esti-mates of present-day CH4 emissions from boreal and arc-tic regions are poorly constrained ranging between 21 and77 Tg CH4 yrminus1 (Saunois et al 2020 Peltola et al 2019Wik et al 2016 Treat et al 2018 McGuire et al 2012Watts et al 2014 Thompson et al 2018 Zhu et al 2015Tan et al 2016 Walter Anthony et al 2016) Estimatesof high-latitude CH4 emissions vary between approacheswith generally lower estimates from atmospheric inversions(top-down estimates) than from field-measured CH4 emis-sions data paired with land cover data (bottom-up estimates)(Saunois et al 2020 McGuire et al 2012) Low accuracyof high-latitude land cover datasets for wetland and lake dis-

tributions and their classification represents a key source ofuncertainty for estimates of high-latitude CH4 emissions andmay contribute to the discrepancies between bottom-up andtop-down estimates A limitation of many currently avail-able land cover datasets is an insufficient differentiation be-tween wetland lake and river classes that are known to havedistinct CH4 emissions (Bruhwiler et al 2021 Bohn et al2015 Marushchak et al 2016 Melton et al 2013)

There are several challenges when using remote sensingapproaches to map distinct wetland lake and river classes atthe circumpolar scale Many small or narrow wetland ecosys-tems with high methane CH4 emissions are located alonglake shorelines along stream networks or in polygonal tun-dra terrain and are thus difficult to map as image resolutioncan be inadequate (Wickland et al 2020 Cooley et al 2017Virtanen and Ek 2014 Liljedahl et al 2016) Wetland de-tection can further be complicated by the presence of treespecies in wetlands eg Scots pine (Pinus sylvestris) blackspruce (Picea mariana) and tamarack (Larix laricina) thatare also found in non-wetland boreal forests making differ-entiation of treed wetlands from non-wetland forests diffi-

Table 1 Continued

221 Wetland classes

222 Lake classes

223 River classes

224 Other classes

31 Wetlands

32 Lakes

33 Rivers

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 3: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

Table 1 Continued

221 Wetland classes

222 Lake classes

223 River classes

224 Other classes

31 Wetlands

32 Lakes

33 Rivers

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 4: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

Table 1 Continued

221 Wetland classes

222 Lake classes

223 River classes

224 Other classes

31 Wetlands

32 Lakes

33 Rivers

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 5: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

Table 1 Continued

221 Wetland classes

222 Lake classes

223 River classes

224 Other classes

31 Wetlands

32 Lakes

33 Rivers

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 6: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

221 Wetland classes

222 Lake classes

223 River classes

224 Other classes

31 Wetlands

32 Lakes

33 Rivers

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 7: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

223 River classes

224 Other classes

31 Wetlands

32 Lakes

33 Rivers

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 8: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

31 Wetlands

32 Lakes

33 Rivers

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 9: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

31 Wetlands

32 Lakes

33 Rivers

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 10: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

31 Wetlands

32 Lakes

33 Rivers

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 11: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

32 Lakes

33 Rivers

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 12: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

33 Rivers

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 13: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

33 Rivers

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 14: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 15: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

34 Other classes

35 Wetscapes

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 16: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 17: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

4 Data availability

5 Conclusions

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 18: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

References

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 19: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 20: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 21: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 22: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 23: The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

Abstract
Introduction
- - Wetland classes
  - Lake classes
  - River classes
  - Other classes
      - Wetlands
        
        Lakes
        
        Rivers
        
        Other classes
        
        Wetscapes
        
        Data availability
        
        Conclusions
        
        Supplement
        
        
        Competing interests
        
        Disclaimer
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Date post:	21-Jan-2023
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The Boreal–Arctic Wetland and Lake Dataset (BAWLD) - ESSD

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