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Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal-Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5° 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 methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ~28 % each of the total wetland area, while the highest methane emitting marsh and tundra wetland classes occupied 5 and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low methane-emitting large lakes (> 10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 and 4 %, respectively. Small (
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The Boreal-Arctic Wetland and Lake Dataset (BAWLD)
David Olefeldt1, Mikael Hovemyr2, McKenzie A. Kuhn1, David Bastviken3, Theodore J. Bohn4, John
Connolly5, Patrick Crill6, Eugénie S. Euskirchen7,8, Sarah A. Finkelstein9, Hélène Genet8, Guido
Grosse10,11, Lorna I. Harris1, Liam Heffernan12, Manuel Helbig13, Gustaf Hugelius2,14, Ryan
Hutchins15, Sari Juutinen16, Mark J. Lara17,18, Avni Malhotra19, Kristen Manies20, A. David McGuire8,
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Susan M. Natali21, Jonathan A. O’Donnell22, Frans-Jan W. Parmentier23,24, Aleksi Ränen25, Christina
Schädel26, Oliver Sonnentag27, Maria Strack28, Suzanne E. Tank29, Claire Treat10, Ruth K. Varner2,30,
Tarmo Virtanen25, Rebecca K. Warren31, Jennifer D. Watts21
1 Department of Renewable Resources, University of Alberta, Edmonton, AB, T6G 2G7, Canada
2 Department of Physical Geography, Stockholm University, 10691 Stockholm, Sweden
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3 Department of Thematic Studies Environmental Change, Linköping University, 58183 Linköping, Sweden
4 WattIQ, 400 Oyster Point Blvd. Suite 414, South San Francisco, CA, 94080, USA
5 Department of Geography, School of Natural Sciences, Trinity College Dublin, Dublin 2, Ireland
6 Department of Geological Sciences, Stockholm University, 10691 Stockholm, Sweden
7 Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
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8 Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
9 Department of Earth Sciences, University of Toronto, Toronto, ON, M5S 3B1, Canada
10 Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Permafrost Research Section, 14473 Potsdam,
Germany
11 Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany
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12 Department of Ecology and Genetics, Uppsala University, 752 36 Uppsala, Sweden
13 Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, B3H 4R2, Canada
14 Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden
15 Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
16 Ecosystems and Environment Research Program, University of Helsinki, FI-00014 Helsinki, Finland
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17 Department of Plant Biology, University of Illinois, Urbana, IL 61801, USA
18 Department of Geography, University of Illinois, Urbana, IL 61801, USA
19 Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
20 U.S. Geological Survey, Menlo Park, CA, USA
21 Woodwell Climate Research Center, Falmouth, MA 02540, USA
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22 Arctic Network, National Park Service, Anchorage, AK 99501 USA
23 Centre for Biogeochemistry in the Anthropocene, Department of Geosciences, University of Oslo, 0315 Oslo, Norway
24 Department of Physical Geography and Ecosystem Science, Lund University, 223 62 Lund, Sweden
25 Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, 00014 University
of Helsinki, Finland
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26 Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, USA
27 Département de Géographie, Université de Montréal, Montréal, QC, Canada
28 Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
29 Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2E9, Canada
30 Department of Earth Sciences and Institute for the Study of Earth, Oceans and Space, University of New Hampshire,
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Durhan, NH 03824, USA
31 National Boreal Program, Ducks Unlimited Canada, Edmonton, AB, T5S 0A2, Canada
Correspondence to: David Olefeldt (olefeldt@ualberta.ca)
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Abstract.
Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and
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associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane
emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future
methane emissions. Here we present the Boreal-Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on
an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography,
soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the
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fractional coverage of five wetland, seven lake, and three river classes within 0.5×0.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 methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux
observations. In BAWLD, wetlands occupied 3.2×106 km2 (14% of domain) with a 95% confidence interval between 2.8 and
3.8×106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ~28% each of the total wetland
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area, while the highest methane emitting marsh and tundra wetland classes occupied 5 and 12%, respectively. Lakes, defined
to include all lentic open-water ecosystems regardless of size, covered 1.4×106 km2 (6% of domain). Low methane-emitting
large lakes (>10 km2) and glacial lakes jointly represented 78% of the total lake area, while high-emitting peatland and
yedoma lakes covered 18 and 4%, respectively. Small (<0.1 km2) glacial, peatland, and yedoma lakes combined covered
17% of the total lake area, but contributed disproportionally to the overall spatial uncertainty of lake area with a 95%
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confidence interval between 0.15 and 0.38×106 km2. Rivers and streams were estimated to cover 0.12 ×106 km2 (0.5% of
domain) of which 8% was associated with high-methane emitting headwaters that drain organic-rich landscapes. Distinct
combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the
mapping of wetscapes that will have characteristic methane emission magnitudes and sensitivities to climate change at
regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake and river extents, and
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which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and
biogeochemical modelling and upscaling efforts for the northern Boreal and Arctic region, in particular those aimed at
improving assessments of current and future methane emissions. Data is freely available at
https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).
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1 Introduction
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Emissions of methane (CH4) from abundant wetlands, lakes, and rivers located in boreal and arctic regions are expected to
substantially increase this century due to rapid climate warming and associated permafrost thaw (Walter Anthony et al.,
2018; Ito, 2019; Hugelius et al., 2020; Schneider von Deimling et al., 2015; Zhang et al., 2017). However, predicting future
CH4 emissions is highly uncertain, as estimates of present-day CH4 emissions from boreal and arctic regions are poorly
constrained, ranging between 21 and 77 Tg CH4 yr-1 (Saunois et al., 2020; Peltola et al., 2019; Wik et al., 2016; Treat et al.,
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2018; McGuire et al., 2012; Watts et al., 2014; Thompson et al., 2018; Zhu et al., 2015; Tan et al., 2016; Walter Anthony et
al., 2016). Estimates of high-latitude CH4 emissions vary between approaches, with generally lower estimates from
atmospheric inversions (top-down estimates), than from field-measured CH4 emissions data paired with land cover data
(bottom-up estimates) (Saunois et al., 2020; McGuire et al., 2012). Low accuracy of high-latitude land cover datasets for
wetland and lake distributions, and their classification, represent key sources of uncertainty for estimates of high-latitude
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CH4 emissions and may contribute to the discrepancies between bottom-up and top-down estimates. A limitation of many
currently available land cover datasets is an insufficient differentiation between wetland, lake, and river classes that are
known to have distinct CH4 emissions (Bruhwiler et al., 2021; Bohn et al., 2015; Marushchak et al., 2016; Melton et al.,
2013).
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There are several challenges when using remote sensing approaches to map distinct wetland, lake, and river classes at the
circumpolar scale. Many small or narrow wetland ecosystems with high methane CH4 emissions are located along lake
shorelines, along stream networks, or in polygonal tundra terrain, and are thus difficult to map as image resolution can be
inadequate (Wickland et al., 2020; Cooley et al., 2017; Virtanen and Ek, 2014; Liljedahl et al., 2016). Wetland detection can
further be complicated by the presence of tree species in wetlands, e.g. Scots pine (Pinus sylvestris), black spruce (Picea
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mariana), and tamarack (Larix laricina), that are also found in non-wetland boreal forests, making differentiation of treed
wetlands from non-wetland forests difficult. Using spectral signatures to differentiate and map distinct wetland classes can
further be difficult due to seasonal variation in inundation or phenology, poor differentiation between ecosystems (e.g.
similarities between different peatland classes), or high spectral diversity within classes due to shifts in vegetation along
subtle environmental gradients (Räsänen and Virtanen, 2019; Vitt and Chee, 1990; Chasmer et al., 2020). Vegetation
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composition and spectral signatures of wetland classes can also vary between different high-latitude regions, e.g. with shifts
in dominant tree and shrub species between North America and Eurasia (Raynolds et al., 2019), and be influenced for
decades by wildfires (Chen et al., 2021; Helbig et al., 2016). Active microwave remote sensing can help detect inundated
wetlands and saturated soils, but has limitations due to its computational requirements, coarse resolution, and issues with
detecting rarely inundated peatlands (Beck et al., 2021; Duncan et al., 2020). Accurate mapping of wetlands that include
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differentiation among distinct wetland classes requires substantial ground truthing, something which has only been done
consistently at local and regional scales (Terentieva et al., 2016; Chasmer et al., 2020; Bryn et al., 2018; Lara et al., 2018;
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Canadian Wetland Inventory Technical Committee, 2016). Similar issues arise for lakes, rivers, and streams. While larger
lakes and rivers have been mapped with high precision (Messager et al., 2016; Linke et al., 2019), the highest CH4 emissions
are generally from ponds, pools, and low-order streams that are too small to be accurately detected by anything other than
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very high-resolution imagery (Muster et al., 2017). Statistical approaches are often used to model the distribution and
abundance of small open-water ecosystems, yielding large uncertainties (Holgerson and Raymond, 2016; Cael and Seekell,
2016; Muster et al., 2019). Remote sensing approaches are also inadequate in assessing other key variables known to
influence lake CH4 emissions, including lake genesis, depth, and sediment characteristics (Messager et al., 2016; Brosius et
al., 2021; Smith et al., 2007; Lara et al., 2021). Another key issue is that wetlands and lakes often are mapped separately,
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allowing for potential double-counting of ecosystems in both wetland and lake inventories (Thornton et al., 2016; Saunois et
al., 2020).
Emissions of CH4 from boreal and arctic ecosystems range from uptake to some of the highest emissions observed globally
(Turetsky et al., 2014; Knox et al., 2019; Glagolev et al., 2011; St Pierre et al., 2019). Net ecosystem CH4 emissions are a
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balance between microbial CH4 production (methanogenesis) and oxidation (methanotrophy), a balance further influenced
by the dominant transport pathway; diffusion, ebullition, and plant-mediated transport (Bridgham et al., 2013; Bastviken et
al., 2004). For wetlands, defined as ecosystems with temporally or permanently saturated soils and biota adapted to anoxic
conditions, CH4 emissions in boreal and arctic regions are primarily influenced by water table position, soil temperatures,
and vegetation composition and productivity (Olefeldt et al., 2013; Treat et al., 2018). Marshes and tundra wetlands are
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characterized by frequent or permanent inundation and dominant graminoid vegetation that enhance methanogenesis and
facilitates plant-mediated transport, and thus generally have high CH4 emissions (Knoblauch et al., 2015; Juutinen et al.,
2003). Conversely, peat-forming bogs and fens generally have a water table at or below the soil surface, less graminoid
vegetation and instead vegetation dominated by mosses, lichens, and shrubs, resulting in typically low to moderate CH4
emissions (Bubier et al., 1995; Pelletier et al., 2007). Permafrost conditions in peatlands can cause the surface to be elevated
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and dry, with cold soil conditions where methanogenesis is inhibited, leading to low CH4 emissions or even uptake
(Bäckstrand et al., 2008; Glagolev et al., 2011). Non-wetland boreal forests and tundra ecosystems generally have net CH4
uptake, as methanotrophy outweighs any methanogenesis (Lau et al., 2015; Juncher Jørgensen et al., 2015; Whalen et al.,
1992). The transition from terrestrial to aquatic ecosystems is not always well defined, and several wetland classification
systems consider shallow, open-water ecosystems as a distinct wetland class (Rubec, 2018). The transition from vegetated to
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open water ecosystems is however associated with shifts in apparent primary controls of CH4 emissions, including a shift
towards increased importance of ebullition (Bastviken et al., 2004). For lakes, when defined to include all lentic open-water
ecosystems regardless of size (e.g. including peatland ponds), spatial variability in CH4 emissions is primarily linked to
water depth and the quantity and origin of the organic matter of the sediment (Heslop et al., 2020; Li et al., 2020). As such,
lake CH4 emissions are generally higher for smaller lakes and for lakes with organic-rich sediments (Wik et al., 2016;
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Holgerson and Raymond, 2016), which are extremely abundant in many high-latitude regions (Muster et al., 2017). The CH4
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emitted from streams and rivers is largely derived from the soils that are drained, and as such emissions generally are higher
in smaller streams draining wetland-rich watersheds (Wallin et al., 2018; Stanley et al., 2016). It is overall likely that studies
of CH4 emissions from boreal and arctic ecosystems have focused disproportionally on sites with higher CH4 emissions
(Olefeldt et al. 2013). A focus on high-emitting sites is warranted for understanding site-level controls on CH4 emissions, but
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may potentially cause bias of bottom-up CH4 scaling approaches if they lack appropriate differentiation between various
wetland and lakes classes in the used land cover datasets.
There is currently no spatial dataset available that has information on the distribution and abundance of wetland, lake, and
river classes defined specifically for the purpose of estimating boreal and arctic CH4 emissions. However, a large number of
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spatial datasets have partial, but relevant, information. This includes circumpolar spatial data of soil types (Hugelius et al.,
2013; Strauss et al., 2017), vegetation (Olson et al., 2001; Walker et al., 2005), surface water extent and dynamics (Pekel et
al., 2016), lake sizes and numbers (Messager et al., 2016), topography (Gruber, 2012), climate (Fick and Hijmans, 2017),
permafrost conditions (Gruber, 2012; Brown et al., 2002), river networks (Linke et al., 2019), and previous estimates of total
wetland cover (Matthews and Fung, 1987; Bartholomé and Belward, 2005). By integrating quantitative spatial data with
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expert knowledge it is possible to model new spatial data for specific purposes (Olefeldt et al., 2016). Researchers with
interests in the boreal and arctic have considerable knowledge of the presence and relative abundance of typical wetland and
lake classes in various high-latitude regions, along with the ability for satellite image interpretation and the judgement to
define parsimonious land cover classes suitable for CH4 scaling.
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Here we present the Boreal-Arctic Wetland and Lake Dataset (BAWLD), an expert knowledge-based land cover dataset.
Developed in concert with a comprehensive dataset of observed CH4 fluxes from high-latitude aquatic ecosystems (Kuhn et
al., 2021), BAWLD includes five wetland, seven lake, and three river classes with distinct CH4 emissions. Coverage of each
wetland, lake, and river class within 0.5° grid cells was modelled through random forest regressions based on expert
assessment data and available relevant spatial data. The approach aims to reduce issues with bias in representativeness of
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empirical data, to reduce issues of overlaps in wetland and lake extents, and to allow for the partitioning of uncertainty of
CH4 emissions estimates to CH4 emission magnitudes or areal extents of different land cover classes. As such, BAWLD will
facilitate improved bottom-up estimates of high-latitude CH4 emissions, and will be suitable for use in process-based models
and as an a-priori input to inverse modelling approaches. The land cover dataset will be suitable for further uses, especially
for questions related to high-latitude hydrology and biogeochemistry. Lastly, BAWLD allows for the definition of
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wetscapes”; regions with distinct co-occurrences of specific wetland and lake classes, and which thus can be used to
understand regional responses to climate change and as a way to visualize the landscape diversity of the boreal and arctic
domain.
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Table 1. Description of data sources and layers extracted into the BAWLD 0.5° grid cell network.
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Dataset and extracted layers
Dataset and extracted layers
WorldClim V2 (Fick and Hijmans, 2017)
- WC2-MAAT: Mean annual average air temperature 1970-2000. (˚C)
- WC2-MAAP: Mean annual average precipitation 1970-2000 (mm)
- WC2-CMI: Climate moisture index 1970-2000 (mm)
Reference information
- LAT: Latitude (˚)
- LONG: Longitude (˚)
- SHORE: Coastal shoreline presence in cell (yes/no)
Circum-Arctic Map of Permafrost and Ground-Ice (Brown et al., 2002)
- CAPG-CON: Continuous permafrost. (%)
- CAPG-DIS: Discontinuous permafrost. (%)
- CAPG-SPO: Sporadic permafrost. (%)
- CAPG-ISO: Isolated permafrost. (%)
- CAPG-XHF: Land with thick overburden and >20% ground-ice. (%)
- CAPG-XMF: Land with thick overburden and 10-20% ground-ice. (%)
- CAPG-XLF: Land with thick overburden and <10% ground-ice. (%)
- CAPG-XHR: Land with thin overburden and >10% ground-ice. (%).
- CAPG-XLR: Land with thin overburden and <10% ground-ice. (%)
- CAPG-REL: Land with relict permafrost. (%)
Northern Circumpolar Soil Carbon Dataset (Hugelius et al., 2014)
- NCS-HSO: Histosol soils; non-permafrost organic soils. (%)
- NCS-HSE Histel soils: permafrost organic soils. (%)
- NCS-AQU: Aqueous soils: non-organic wetland soils. (%)
- NCS-ROC: Rocklands. (%)
- NCS-GLA: Glaciers. (%)
- NCS-H2O: Open water. (%)
BasinATLAS (Linke et al., 2019)
- BAS-RIV: River area. (%)
Global Lakes and Wetland Dataset (Lehner and Döll, 2004)
- GLWD-RIV: Rivers, 6th order rivers or greater. (%)
Circumpolar Arctic Vegetation Map (CAVM Team 2003)
- CAVM-BAR: Barren Tundra. (%)
- CAVM-GRA: Graminoid Tundra. (%)
- CAVM-SHR: Shrubby Tundra. (%)
- CAVM-WET: Wet Tundra. (%)
Terrestrial Ecoregions of the World (Olson et al., 2001)
- TEW-BOR: Fractional cover of boreal ecoregion/ (%)
- TEW-TUN: Fractional cover of tundra ecoregion. (%)
- TEW-GLA: Fractional cover of glaciers. (%)
HydroLakes (Messager et al., 2016)
- HL-LAR: Lakes >10 km2. (%)
- HL-MID: Lakes between 10 km2 and 0.1 km2. (%)
- HL-SHO: Shoreline density (length/area) of lakes >0.1 km2. (m/m2)
Global Land Cover Database 2000 (Bartholomé and Belward, 2005)
- GLC2-H2O: Water Bodies, natural and artificial. (%)
- GLC2-RFSM: Regularly Flooded Shrub and/or Herbaceous Cover. (%)
- GLC2-FOR: Forest cover. (%)
Global Inundation Map (Fluet-Chouinard et al., 2015)
- GIM-MAMI: Mean annual minimum inundation. (%)
- GIM-MAMA: Mean annual maximum inundation. (%)
Dataset of Ice-Rich Yedoma Permafrost (Strauss et al., 2017)
- IRYP-YED: Yedoma ground. (%)
GlobLand30 (Chen et al., 2015)
- GL30-H2O: Water bodies: including lakes, rivers, reservoirs. (%)
- GL30-WET: Wetlands: marshes, floodplains, shrub wetland, peatlands. (%)
- GL30-TUN: Tundra: shrub, herbaceous, wet, and barren tundra. (%)
- GL30-ART: Artificial Surfaces: cities, industry, transport. (%)
- GL30-ICE: Permanent snow and ice. (%)
Permafrost zonation and Terrain Ruggedness Index (Gruber, 2012)
- PZI-PERM: Permafrost ground. (%)
- PZI-FLAT: Flat topography. (%)
- PZI-UND: Undulating topography. (%)
- PZI-HILL: Hilly topography. (%)
- PZI-MTN: Mountainous topography. (%)
- PZI-RUG: Rugged topography. (%)
Global Surface Water (Pekel et al., 2016)
- GSW-RAR: Rarely inundated; open water in 0 to 5% of occasions. (%)
- GSW-OCC: Occasionally inundated; open water 5 to 50%. (%)
- GSW-REG: Regularly inundated; open water 50 to 95%. (%)
- GSW-PER: Permanent open water; open water 95 to 100%. (%)
Global Wetlands (Matthews and Fung, 1987)
-GWET-IN: Inundation and presence of wetlands. (%).
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2 Development of the Boreal-Arctic Wetland and Lake Dataset
2.1 Study domain and harmonization of available spatial data
The BAWLD domain includes all of the northern boreal and tundra ecoregions, and also areas of rock and ice at latitudes
>50°N (Olson et al., 2001). The BAWLD domain thus covers 25.5×106 km2, or 17% of the global land surface. Although
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northern peat-forming wetlands can also be found in temperate ecoregions, our decision to define the southern limit of
BAWLD by the transition from boreal to temperate ecoregions was based on the greater human footprint and the increased
biogeographic diversity of temperate ecoregions, which would require additional land cover classes (Venter et al., 2016). A
network of 0.5° grid cells, cropped along coasts and at the transition from boreal to temperate ecoregions, was created for the
BAWLD domain.
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Grid cells in BAWLD were populated with data from 15 publicly available spatial datasets, yielding 53 variables with spatial
information (Table 1). Most datasets that were included have data at higher resolution than the 0.5° BAWLD grid cells,
hence information was averaged for each grid cell. For datasets where the spatial resolution was coarser or where spatial data
were not aligned with the 0.5° grid cells, data was first apportioned into BAWLD grid cells before area-weighted averages
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were calculated. Climate data from the WorldClim2 (WC2) dataset (Fick and Hijmans, 2017) were averaged for each grid
cell, including mean annual air temperature, mean annual precipitation, and climate moisture index. Information on
soils and permafrost conditions were summarized as fractional coverage within each grid cell, and included permafrost
extent from the Permafrost Zonation and Terrain Ruggedness Index (PZI) dataset (Gruber, 2012), permafrost zonation,
ground ice content, and overburden thickness from the Circum-Arctic Map of Permafrost and Ground-Ice (CAPG) dataset
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(Brown et al., 2002), yedoma ground from the Ice-Rich Yedoma Permafrost (IRYP) dataset (Strauss et al., 2017), and non-
permafrost peat “histosol”, permafrost peat “histel”, and “aqueous” wetland soils from the Northern Circumpolar Soil
Carbon Database (NCSCD; hereafter NCS) (Hugelius et al., 2013). Four independent datasets provided information on
wetland coverage, although without further differentiation between distinct wetland classes; the “regularly flooded shrub
and/or herbaceous cover area from the Global Land Cover Database 2000 (GLC2) (Bartholomé and Belward, 2005), the
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“wetlands” area in the GlobLand30 (GL30) dataset (Chen et al., 2015), and the “inundation and presence of wetlands” area
from the Global Wetlands (GWET) dataset (Matthews and Fung, 1987), and the Circumpolar Arctic Vegetation Map
(CAVM) dataset (Walker et al., 2005). Two datasets provided information of the extent of forested regions; the GLC2 and
the Terrestrial Ecoregions of the World (TEW) dataset (Olson et al., 2001), while three datasets provided information on the
extents of tundra vegetation; the CAVM, the GL30, and the TEW. Three datasets provided information on extent of glaciers
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and permanent snow; the NCS, the GL30, and the TEW. The NCS dataset also provided information about the extents of
rocklands”, while the PZI dataset had extents of topographic ruggedness (flat, undulating, hilly, mountainous, and
rugged). Information on river extents was found in two datasets; the “river area” in the BasinATLAS (BAS) dataset (Linke
et al., 2019), and “rivers” in the Global Lakes and Wetland (GLW) dataset, which includes 6th order rivers and greater
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(Lehner and Döll, 2004). Inundation dynamics was provided by two datasets, with “mean annual minimum” and “mean
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annual maximum” inundation in the Global Inundation Map (GIM) dataset (Fluet-Chouinard et al., 2015), and an analysis of
temporal inundation from the Global Surface Water (GSW) dataset (Pekel et al., 2016) where we defined inundation of
individual 30 m pixels as being inundated rarely (>0 to 5% of all available Landsat images), occasionally (5 to 50%),
regularly (50 to 95%), or permanently (95 to 100%). Four datasets included information about static extents of open
water, including “open water” in NCS, “water bodies” in GL30, “water bodies” in GLC2, and information about lakes in the
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Hydrolakes (HL) dataset (Messager et al., 2016), where we differentiated between the area of “large lakes” (lakes >10 km2),
and “midsize lakes” (lakes between 0.1 and 10 km2).
2.2 Land cover classes in BAWLD
The land cover classification in BAWLD was constructed with the goal to enable upscaling of CH4 fluxes for large spatial
extents. As such, we aimed to include as few classes as possible to facilitate for large-scale mapping, while still including
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classes that allow for separation among ecosystems with distinct hydrology, ecology, biogeochemistry and thus net CH4
fluxes. The BAWLD land cover classification is hierarchical; with five wetland classes, seven lake classes, and three river
classes, along with four other classes; glaciers, dry tundra, boreal forest, and rocklands. The class descriptions below were
provided to all experts for their land cover assessments, and thus effectively serve as the BAWLD class definitions.
2.2.1 Wetland Classes
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Wetlands are defined by having a water table near or above the land surface for sufficient time to cause the development of
wetland soils (either mineral soils with redoximorphic features, or organic soils with > 40 cm peat), and the presence of plant
species with adaptations to wet environments (Hugelius et al., 2020; Canada Committee on Ecological (Biophysical) Land
Classification et al., 1997; Jorgenson et al., 2001). Wetland classifications for boreal and arctic biomes can focus either on
small-scale wetland classes that have distinct hydrological regimes, vegetation composition, and biogeochemistry, or on
225
larger-scale wetland complexes that are comprised of distinct patterns of smaller wetland and open-water classes
(Gunnarsson et al., 2014; Terentieva et al., 2016; Masing et al., 2010; Glaser et al., 2004). While larger-scale wetland
complexes are easier to identify through remote sensing techniques (e.g. patterned fens comprised of higher elevation ridges
and inundated hollows), our classification focuses on wetland classes due to greater homogeneity of hydrological, ecological,
and biogeochemical characteristics that regulate CH4 fluxes (Heiskanen et al., 2021).
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Several boreal countries identify four main wetland classes, differentiated primarily based on hydrodynamic
characterization; bogs, fens, marshes, and swamps (Gunnarsson et al., 2014; Canada Committee on Ecological (Biophysical)
Land Classification et al., 1997; Masing et al., 2010). The BAWLD classification follows this general framework, but further
uses the presence or absence of permafrost as a primary characteristic for classification and excludes a distinct swamp class,
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yielding five classes; Bogs, Fens, Marshes, Permafrost Bogs, and Tundra Wetlands (Figure 1). The swamp class was omitted
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due to the wide range of moisture and nutrient conditions of swamps, as well as the limited number of studies of swamp CH4
fluxes (Kuhn et al., 2021). We instead included swamp ecosystems in expanded descriptions of Bogs, Fens, and Marshes.
The presence or absence of near-surface permafrost was used as a primary characteristic to distinguish between Permafrost
Bogs and Bogs, and to distinguish Tundra Wetlands from Marshes and Fens. The presence or absence of near-surface
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permafrost is considered key for controlling CH4 emissions given its influence on hydrology, and for the potential of
permafrost thaw and thermokarst collapse to cause rapid non-linear shifts to CH4 emissions (Bubier et al., 1995; Turetsky et
al., 2002; Malhotra and Roulet, 2015). Finally, while some classifications include shallow (e.g. 2 m depth), open-water
ecosystems within the definition of wetlands (Gunnarsson et al., 2014; Canada Committee on Ecological (Biophysical) Land
Classification et al., 1997), we have included all open-water ecosystems without emergent vegetation within the lake classes
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(see below) due to the strong influence of emergent vegetation in controlling CH4 emissions (Juutinen et al., 2003).
Figure 1. Descriptions of wetland classes in BAWLD as distinguished based on the moisture regime, the nutrient/pH regime,
hydrodynamics, and the presence/absence of permafrost.
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Bogs are described as ombrotrophic peatland ecosystems, i.e. only dependent on precipitation, and snowmelt for water
inputs. Peat thickness is at least 40 cm, with maximum thickness > 10 m. The peat profile is not affected by permafrost,
although in some climatically colder settings there may be permafrost below the peat profile. Bogs are wet to saturated
ecosystems, often with small-scale (<10 m) microtopographic variability, with stagnant water and a water table that rarely is
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above the surface or more than 50 cm below the surface (Figure 1). Bogs have low pH (<5), low concentrations of dissolved
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ions, and low nutrient availability resulting from a lack of hydrological connectivity to surrounding mineral soils. Vegetation
is commonly dominated by Sphagnum mosses, lichens, and woody shrubs, and can be either treed or treeless (Beaulne et al.,
2021). Our description of Bogs also includes what is commonly classified as treed swamps, which generally represent
ecotonal transitions between peatlands and upland forests (Canada Committee on Ecological (Biophysical) Land
260
Classification et al., 1997). Emissions of CH4 from Bogs are low to moderate in comparison with other classes (Kuhn et al.,
2021).
Fens are described as minerotrophic peatland ecosystems, i.e. hydrologically connected to surrounding mineral soils through
surface water or groundwater inputs. A Fen peat profile is at least 40 cm thick (Gorham et al. 1991), although maximum
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thickness is generally less than for bogs. The peat profile is not affected by permafrost. Fens are wet to saturated ecosystems,
with generally slow-moving water (Figure 1). Fens have widely ranging nutrient regimes and levels of dissolved ions
depending on the degree and type of hydrological connectivity to their surroundings, ranging from poor fens to rich fens.
Vegetation largely depends on wetness and nutrient availability, where more nutrient poor fens can have Sphagnum mosses,
shrubs, and trees, while rich fens are dominated by brown mosses, graminoids (sedges, rushes), herbaceous plants, and
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sometimes coniferous or deciduous trees (e.g. willows, birch, larch). Our description of Fens also includes what is
commonly classified as shrubby swamps, which often are associated with riparian ecotones and lake shorelines. Fen CH4
emissions are moderate to high in comparison to other classes (Kuhn et al., 2021).
Marshes are minerotrophic wetlands with dynamic hydrology, and often high nutrient availability (Figure 1). Vegetation is
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dominated by emergent macrophytes, including tall graminoids such as rushes, reeds, grasses and sedges some of which
can persist in settings with >1.5 m of standing water. Marshes are saturated to inundated wetlands, often with highly
fluctuating water levels as they generally are located along shorelines of lake or coasts, along streams and rivers, or on
floodplains and deltas. It is common for marshes to exhibit both flooded and dry periods. Dry periods facilitate
decomposition of organic matter, and can prevent the build-up of peat. As such Marshes generally have mineral soils,
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although some settings allow for the accumulation of highly humified organic layers sometimes indicating ongoing
succession towards a peatland ecosystem. Salinity can vary depending on water sources, with brackish to saline conditions in
some areas of groundwater discharge, or in coastal settings. While highly saline marshes can have low CH4 emissions due to
inhibition of methanogenesis, Marshes generally have high to very high CH4 emissions compared to other classes (Kuhn et
al., 2021).
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Permafrost Bogs are peatland ecosystems, although the peat thickness in cold climates is often relatively shallow.
Permafrost Bogs have a seasonally thawed active layer that is 30 to 70 cm thick, with the remainder of the peat profile
perennially frozen (i.e. permafrost). Excess ground-ice and ice expansion often elevate Permafrost Bogs up to a few meters
above their surroundings, and as such they are ombrotrophic and generally the wetland class with the driest soils (Figure 1).
290
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Permafrost Bogs have moist to wet soil conditions, often with a water table that follows the base of the seasonally
developing thawed soil layer. Ombrotrophic conditions cause nutrient-poor conditions, and the vegetation is dominated by
lichens, Sphagnum mosses, woody shrubs, and sometimes stunted coniferous trees. Permafrost Bogs are often interspersed in
a fine-scale mosaic (10 to 100 m) with other wetland classes, e.g. Bogs and Fens. Common Permafrost Bog landforms
include palsas, peat plateaus, and the elevated portions of high- and low-center polygonal peatlands. Permafrost Bogs have
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very low CH4 emissions, and sometimes even CH4 uptake (Kuhn et al., 2021).
Tundra Wetlands are treeless ecosystems with saturated to inundated conditions, most commonly with near surface
permafrost (Figure 1). Tundra Wetlands can have either mineral soils or shallow organic soils, and generally receive surface
or near-surface waters from their surroundings, as permafrost conditions preclude connectivity to deeper groundwater
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sources. Vegetation is dominated by short emergent vegetation, including sedges and grasses, with mosses and shrubs in
slightly drier sites. Tundra Wetlands have lower maximum depth of standing water than Marshes, due to the shorter
vegetation. Tundra Wetlands can be found in basin depressions, in low-center polygonal wetlands, and along rivers, deltas,
lake shorelines, and on floodplains in regions of continuous permafrost. Despite the name, limited wetlands with these
characteristics (hydrology, permafrost conditions, and vegetation) can also be found within the continuous permafrost zone
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in boreal and sub-arctic regions (Virtanen et al., 2016). Tundra Wetlands have moderate to high CH4 emissions compared to
other classes (Kuhn et al., 2021).
2.2.2 Lake Classes
Lakes in BAWLD are considered to include all lentic open-water ecosystems, regardless of surface area and depth of
standing water. It is common in ice-rich permafrost lowlands and peatlands for open-water bodies to have shallow depths,
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often less than two meters, even when surface areas are up to hundreds of km2 in size (Grosse et al., 2013). While small,
shallow open-water bodies often are included in definitions of wetlands (Canada Committee on Ecological (Biophysical)
Land Classification et al., 1997; Gunnarsson et al., 2014; Treat et al., 2018), we include them here within the lake classes as
controls on net CH4 emissions depend strongly on the presence or absence of emergent macrophytes (Juutinen et al., 2003).
Further classification of lakes in BAWLD is based lake size and lake genesis where lake genesis influences lake bathymetry
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and sediment characteristics. Previous global spatial inventories of lakes include detailed information on size and location of
individual larger lakes (Messager et al., 2016; Downing et al., 2012), but do not include open-water ecosystems <0.1 km2 in
size, and do not differentiate between lakes of different genesis (e.g. tectonic, glacial, organic, and yedoma lakes). Small
water bodies are disproportionately abundant in some high latitude environments (Muster et al., 2019), have high emissions
of CH4 (Holgerson and Raymond, 2016), and therefore require explicit classification apart from larger water-bodies.
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Furthermore, lake genesis and sediment type haven been shown to influence net CH4 flux from lakes (Wik et al., 2016). In
BAWLD we thus differentiate between large (>10 km2), midsize (0.1 to 10 km2) and small (<0.1 km2) lake classes, and
further differentiate between three lake types for midsize and small lakes; peatland, yedoma, and glacial lakes.
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Small and Midsize Peatland Lakes are described as lakes with thick organic sediments that are mainly found adjacent to or
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surrounded by peatlands, or in lowland tundra regions with organic-rich soils. Small Peatland Lakes includes the numerous
small pools often found in extensive peatlands and lowland tundra regions, e.g. including the open-water parts of string fens
and polygonal peatlands. Peatland Lakes generally form as a result of interactions between local hydrology and the
accumulation of peat which can create open water pools and lakes (Garneau et al., 2018; Harris et al., 2020), but can also
form in peatlands as a result of permafrost dynamics (Liljedahl et al., 2016; Sannel and Kuhry, 2011). As such, these lakes
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with thick organic sediments are often shallow and have a relatively low shoreline development index. Peatland lakes have
dark waters with high concentrations of dissolved organic carbon. Small Peatland Lakes generally have higher CH4
emissions than Midsize Peatland Lakes, but observations from both classes range from moderate to very high CH4
emissions, with roughly half of emissions attributed to ebullition (Kuhn et al., 2021).
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Small and Midsize Yedoma Lakes are exclusive to non-glaciated regions of eastern Siberia, Alaska, and the Yukon where
yedoma deposits accumulated during the Pleistocene (Strauss et al., 2017). Yedoma permafrost soils are ice-rich and contain
fine-grained, organic-rich loess which was deposited by wind and accumulated upwards in parallel with permafrost
aggradation, thus limiting decomposition and facilitating organic matter burial (Schirrmeister et al., 2013). Notable
thermokarst features, including lakes, often develop when yedoma permafrost thaws, causing labile organic matter to
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become available for microbial mineralization (Walter Anthony et al., 2016). Small Yedoma lakes typically represent
younger thermokarst features, whereas Midsized Yedoma Lakes represent later stages of thermokarst lake development.
Small Yedoma Lakes are thus more likely to have actively thawing and expanding lake edges where CH4 emissions can be
extremely high, largely driven by high ebullition emissions (Walter Anthony et al., 2016). Century-scale development of
yedoma lakes can shift the main source of CH4 production from yedoma deposits to new organic-rich sediment that
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accumulate from allochtonous and autochthonous sources resulting in such lakes here being considered as Peatland Lakes.
Midsize Yedoma Lakes have lower CH4 emissions than Midsize Peatland Lakes, while Small Yedoma Lakes have similar
CH4 emissions as Small Peatland Lakes albeit with a greater proportion attributed to ebullition (Kuhn et al., 2021).
Small and Midsize Glacial Lakes include all lakes with organic-poor sediments predominately those formed through glacial
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or post-glacial processes, e.g. kettle lakes and bedrock depressions. However, due to similarities in CH4 emissions and
controls thereof, we also include all other lakes with organic-poor sediments within these classes. Glacial Lakes typically
have rocky bottoms or mineral sediments with limited organic content. Lakes in this class are abundant on the Canadian
Shield and in Fennoscandia, but can be found throughout the boreal and tundra biomes. Many Glacial Lakes have high
shoreline development index, with irregular, elongated shapes. Generally, Glacial Lakes are deeper than lakes in the other
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classes, when comparing lakes with similar lake area. Glacial Lakes have very low to moderate CH4 emissions, with slightly
greater emissions from Small than Midsize Glacial Lakes, driven by greater ebullitive emissions (Kuhn et al., 2021).
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Large Lakes are greater than 10 km2 in surface area. Most Large Lakes are glacial or structural/tectonic in origin. Lake
genesis is not considered for further differentiation within this land cover class. Emissions of CH4 from Large Lakes are very
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low to low (Kuhn et al., 2021).
2.2.3 River Classes
We include three river classes in BAWLD, Large Rivers, Small Organic-Rich Rivers, and Small Organic-Poor Rivers. Large
Rivers are described as 6th Strahler order rivers or greater, and generally have river widths >~75 m (Downing et al., 2012;
Lehner and Döll, 2004). Small Organic-Rich Rivers include all 1st to 5th order streams and rivers that drain peatlands or other
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wetland soils, thus being associated with high concentrations of dissolved organic carbon and high supersaturation of CH4.
Conversely, Small Organic-Poor Rivers drain regions with less wetlands and organic-rich soils, and generally have lower
concentrations of dissolved organic carbon and dissolved CH4.
2.2.4 Other Classes
Four additional classes are included in BAWLD; Glaciers, Rocklands, Dry Tundra, and Boreal Forests. Glaciers include
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both glaciers and other permanent snow and ice on land. Rocklands include areas with very poor soil formation and where
vegetation is largely absent. Rocky outcrops in shield landscapes, slopes of mountains, and high Arctic barren landscapes are
included in the class. The Rocklands class also includes artificial surfaces such as roads and towns. Glaciers and Rocklands
are largely considered to be neutral with respect to CH4 emissions. The Dry Tundra class includes both lowland arctic tundra
and alpine tundra; both treeless ecosystems dominated by graminoid or shrub vegetation. Dry Tundra ecosystems generally
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have near-surface permafrost, with seasonally thawed active layers between 20 and 150 cm depending on climate, soil
texture, and landscape position (van der Molen et al., 2007; Heikkinen et al., 2004). Near-surface permafrost in Dry Tundra
prevents vertical drainage, but lateral drainage ensures predominately oxic soil conditions. A water table is either absent or
close to the base of the seasonally thawing active layer. Dry Tundra is differentiated from Permafrost Bogs by having
thinner organic soil (<40 cm), and from Tundra Wetlands by their drained soils (average water table position >5 cm below
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soil surface). Dry Tundra generally have net CH4 uptake, but very low CH4 emissions are also common (Kuhn et al., 2021).
Boreal Forests are treed ecosystems with non-wetland soils. Coniferous trees are dominant, but the class also includes
deciduous trees in warmer climates and landscape positions. Boreal Forests may have permafrost or non-permafrost ground,
where absence of permafrost often allow for better drainage. Overall, it is rare for anoxic conditions to occur in Boreal
Forest soils, and CH4 uptake is prevalent, although low CH4 emissions have been observed during brief periods during
385
snowmelt or following summer storms (Matson et al., 2009), or conveyed through tree stems and shoots (Machacova et al.,
2016). The Boreal Forest class also includes the few agricultural/pasture ecosystems within the boreal biome.
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2.3 Expert assessment
Expert assessments can be used to inform various environmental assessments, and are particularly useful to assess levels of
uncertainty and to provide data that cannot be obtained through other means (Olefeldt et al., 2016; Loisel et al., 2021; Abbott
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et al., 2016; Sayedi et al., 2020). We solicited an expert assessment to aid in the modelling of fractional coverage of the 19
land cover classes within each BAWLD grid cell. Researchers associated with the Permafrost Carbon Network
(www.permafrostcarbon.org) with expertise from wetland, lake, and/or river ecosystems within the BAWLD domain were
invited to participate. We also included a few additional referrals to suitable experts outside the Permafrost Carbon Network.
A total of 29 researchers completed the expert assessment, and are included as co-authors of the BAWLD dataset. Each
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expert was asked to identify a region within the BAWLD domain for which they considered themselves familiar. Experts
were then assigned 10 random cells from their region of familiarity and 10 cells distributed across the BAWLD domain that
allowed for an overall balanced distribution of training cells (Figure S1). No cell was assessed more than once, and in total
~3% of the area of the BAWLD domain was included in the expert assessment. Each expert was asked to assess the percent
coverage of each of the 19 land cover classes within their 20 training cells. To guide their assessment, each expert was
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provided step-by-step instructions, plus information on the definitions of each land cover class, and a KML file with the data
extracted from available spatial datasets for each grid cell (Table 1). Experts were asked to use their knowledge of typical
wetland and lake classes within specific high-latitude regions, their ability to interpret satellite imagery as provided by
Google Earth, and their judgement of the quality and relevance of available spatial datasets to make their assessments of
fractional cover. The information provided to experts to carry out the assessment is provided in the Supplementary
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Information.
2.4 Random forest model and uncertainty analysis
Random forest regression models were created to predict the percent coverage of all 19 individual BAWLD land cover
classes, along with three additional models for total wetland, lake, and river coverage. Each land cover class was at first
modelled separately, which was followed by minor adjustments, described below, that ensured that the total land cover
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within each cell added up to 100%. All statistical analysis and modelling were done using R 4.0.2 (R Core Team, 2020), and
the packages Boruta (v7.0.0; Miron et al., 2010), caret (v6.0-86; Kuhn, 2020), randomForest (v4.6-14; Liaw and Wiener,
2002), and factoextra (v1.0.7; Kassambara and Mundt, 2020).
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Table 2. Summary of random forest models for each land cover class in BAWLD.
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Land cover classes
RMSE (%)
%Var
mtrya
Var.b
Relative Variable Importancec
Glaciers
2.32
95.9
13
24
GL30-ICE (100) NCS-GLA (6)
Rocklands
9.79
67.2
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
14.7
75.2
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
15.5
79.8
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
8.5
85.8
25
48
GL30-WET (100) NCS-HSE (46) NCS-HSO (44) PZI_FLAT (28)
GWET-IN (10) WC2-MAAT (6) GLC2-WET (5)
Bog
4.7
75.0
22
42
NCS-HSO (100) GL30-WET (47) WC2-MAAT (23) PZI-PERM (17)
PZI_FLAT (12) GLC2-WET (12) WC2-MAAP (6)
Fen
4.5
76.3
21
40
NCS-HSO (100) GL30-WET (56) WC2-MAAT (16) PZI-PERM (7)
Marsh
1.3
54.1
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
4.1
84.0
22
42
NCS-HSE (100) CAPG_DIS (6) CAPG-XMF (5)
Tundra Wetland
4.1
47.2
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
2.03
97.8
32
32
GL30-H2O (100)
Large Lake
0.75
99.5
30
30
HL-LAR (100) GL30-H2O (18) NCS-H2O (10)
Midsize Glacial Lake
1.49
75.3
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
1.44
68.5
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
0.86
68.4
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
0.89
15.6
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
0.47
39.2
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
1.22
65.9
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
0.49
90.3
16
31
GLWD-RIV (100) BAS-RIV (39) GSW-OCC (11)
Large River
0.48
90.4
17
32
GLWD-RIV (100) BAS-RIV (39) GSW-OCC (10)
Small Org.-Poor Rivers
0.09
18.7
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 Org.-Rich Rivers
0.04
59.3
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)
amtry is a fitted variable which decides how many variables that were randomly chosen at each split in the random forest analysis.
bVar. indicates the number of variables that were included (out of 53) in the random forest analysis after the Boruta automatic feature selection.
cRelative variable importance the most influential variable in the random forest analysis is assigned a 100% rating, and the importance of other variables
are relative to this. See Table 1 for full descriptions of the variables. Here we list either all variable with >5% influence, or the top ten variables.
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Prior to running the random forest analyses, we performed an automatic feature selection using a Boruta algorithm, (Miron et
al., 2010). The Boruta algorithm completed 150 runs for each land cover class, after which subsets of the 53 possible data
variables (Table 1) were deemed important and selected for inclusion in subsequent random forest models (Table 2). The
random forest models (Kuhn 2020, Liaw and Wiener, 2002) then used boot-strapped samples (i.e. the expert assessments of
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land cover fractional grid cell coverages) to grow 500 decision-trees (ntree), with a subset of randomized data variables as
predictors at each tree node (mtry). We used a 10-fold cross-validation with five repetitions providing mtry as a tuneable
parameter for model training. The random forest model output included the root mean squared error (RMSE), the percent of
the expert assessment variability that was explained (%Var), and relative variable importance (Table 2). Relative variable
importance assigns a 100% importance to the variable with the most influence on the model, and then ranks all other
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variables relative to the influence of that variable. A bias correction (Song, 2015) was applied to the predicted data of land
cover class coverages, as the models were found to overestimate low coverages and underestimate high coverages. After the
bias correction, all bias-adjusted predictions <0% were set to 0%, while those >100% were set to 100% (for examples of the
bias correction, see Figure S2). Next, we ensured that the combined coverage of all 19 land cover classes within each grid
cell added up to 100% by applying a proportional adjustment. In order to estimate the 5th and 95th percentile confidence
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bounds of the land cover predictions, we repeated the random forest analysis, as outlined above, an additional 20 times for
each class. Each new run completely excluded 20% of the expert assessments, and the data were reshuffled four times. Each
grid cell thus had 21 predictions of coverage for each of the 19 land cover classes, and for the cumulative wetland, lake, and
river coverages, and the variability of these predictions were used to define the 5th and 95th percentile confidence bounds.
435
While each cell in BAWLD has a distinct land cover combination, we were also interested in identifying cells with
similarities in their land cover compositions to distinguish between regions of the boreal and arctic domain that represent
characteristic landscapes. We carried out a k-means clustering (Kassambara and Mundt, 2020) to group grid cells with
similarities in their predicted land cover compositions. The k-means clustering was based on within-cluster sum of squares,
and we evaluated resulting maps with between 10 and 20 distinct classes. Using 15 clusters was deemed to balance within-
440
cluster sum of squares and interpretability of the resulting map. As each cluster was defined largely by the relative
dominance (or absence) of different wetland, lake, and river classes, we henceforth refer to these clusters as “wetscapes”.
2.5 Evaluation against regional wetland datasets
We evaluated the predictions of wetland coverage in BAWLD against four independent, high-resolution regional land cover
datasets. These four datasets were chosen as they included more than one wetland class, thus enabling both evaluation
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against total wetland coverage and subsets of wetland classes. Two of these datasets were specifically aimed at mapping of
wetlands, including Ducks Unlimited Canada’s wetland inventories for western Canada as part of the Canadian Wetland
Inventory (CWI; Canadian Wetland Inventory Technical Committee, 2016), and wetland mapping of the West Siberian
Lowlands (WSL) (Terentieva et al., 2016). The other two datasets, the 2016 National Land Cover Database (NLCD) of
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Alaska (Homer et al., 2020), and the 2018 CORINE Land Cover (Büttner, 2014) of northern Europe, represent more general
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land cover datasets. Data from these four datasets were summarized for each BAWLD grid cell where there was complete
coverage. Data filtration was done for the CWI to remove cells if >10% of the cell was classified as burned, cloud, or
shadow. There were few cases where there were equivalent wetland classes in BAWLD and these four regional datasets, and
as such comparisons were generally made between groups of wetland classes that were considered generally comparable.
Similar evaluations were not possible for the lake classes, as there are no regional or circumpolar spatial datasets with
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information on lake genesis or sediment type.
3 Results and Discussion
The fractional land cover estimates of the Boreal-Arctic Wetland and Lake Dataset (BAWLD) is freely available online at
https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021), and includes both the central estimates and the 95% high and
low estimates of each land cover class in each grid cell.
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3.1 Wetlands
Wetlands were predicted to cover a total of 3.2 × 106 km2, or 12.5% of the BAWLD domain. The wetland area was
dominated by Fens (29% of total wetland area), Bogs (28%), and Permafrost Bogs (27%), while Marshes and Tundra
Wetlands, which have relatively higher CH4 emissions, covered 5 and 12% of the wetland area, respectively (Table 3). This
estimate of total wetland area was greater than previously mapped within the BAWLD domain in GLC2 at 0.9 × 106 km2
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(Bartholomé and Belward, 2005), GL30 at 1.4 ×106 km2 (Chen et al., 2015), and GWET at 2.3 × 106 km2 (Matthews and
Fung, 1987), but similar to the area of wetland soils in NCS (sum of “histosols”, “histels”, and “aqueous” soil coverage) at
3.0 × 106 km2 (Hugelius et al., 2014). Differences between BAWLD and other estimates of wetland area likely stem partially
from differences in wetland definitions, where e.g. definitions of wetlands in GLC2 and GL30 likely do not include wooded
bogs, fens, and permafrost bogs. While estimates of total wetland area GWET and in the NCS were closer to BAWLD, there
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were differences in the spatial distribution. Wetland cover in BAWLD was generally greater than in GWET and NCS in
regions with low wetland cover. This likely reflects the ability of experts to infer the presence of small, or transitional
wetlands that may otherwise be underestimated when mapped using other methodologies. Conversely, wetland cover in
BAWLD was generally lower than in GWET and NCS in regions with high wetland cover. This was likely due to
differences in definitions, especially the exclusion of all open water ecosystems from wetlands in BAWLD. For example, it
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was common in the West Siberian Lowlands for the summed coverage of wetland soils in NCS and the open water
coverage in GL30 to be substantially greater than 100%, suggesting that NCS included peatland pools and small ponds
within its wetland soil coverage. Overall, the predictive random forest model of total wetland coverage was able to explain
86% of the variability in the expert assessments, and it was primarily influenced by the area of “wetlands” in GLC30 and the
wetland soil categories in NCS, followed by the coverage of flat topography in PZI (Gruber, 2012) (Table 2).
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Table 3. Summary of central estimates, 95% low and high confidence bounds, and the range of the 95% confidence interval
expressed as a percent of the central estimate, for each of the land cover classes within the BAWLD domain.
Land cover classes
Central
estimate
(106 km2)
Low
confidence
bound
(106 km2)
High
confidence
bound
(106 km2)
95%
confidence
interval
(106 km2)
95% CI
(% of central
estimate)
Glaciers
2.09
1.99
2.21
0.22
11
Rocklands
2.74
2.21
3.40
1.19
44
Tundra
5.28
4.56
6.37
1.82
34
Boreal Forest
10.66
9.77
11.39
1.61
15
Wetlands
3.18
2.79
3.79
1.00
31
Bog
0.88
0.71
1.24
0.53
60
Fen
0.91
0.76
1.14
0.38
42
Marsh
0.16
0.12
0.23
0.11
71
Permafrost Bog
0.86
0.67
1.17
0.50
58
Tundra Wetland
0.38
0.31
0.53
0.22
59
Lakes
1.44
1.34
1.59
0.24
17
Large Lake
0.64
0.61
0.72
0.11
18
Midsize Peatland Lake
0.14
0.11
0.21
0.10
69
Midsize Yedoma Lake
0.034
0.023
0.071
0.05
140
Midsize Glacial Lake
0.38
0.33
0.43
0.10
26
Small Peatland Lake
0.12
0.085
0.17
0.08
71
Small Yedoma Lake
0.028
0.015
0.046
0.03
114
Small Glacial Lake
0.094
0.051
0.16
0.11
119
Rivers
0.12
0.094
0.19
0.10
81
Large River
0.080
0.072
0.11
0.04
50
Small Organic-Rich Rivers
0.010
0.005
0.054
0.05
502
Small Organic-Poor Rivers
0.033
0.020
0.067
0.05
143
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The predictive random forest models for individual wetland classes differed both in terms of how much of the variability of
the expert assessment data was explained, and in terms of which spatial data were most influential (Table 2). The model for
Permafrost Bog coverage explained 84% of the variability in the expert assessments and was very strongly influenced by
histel distribution in the NCS (Hugelius et al., 2014). Predictive models explained ~75% of the variability in the expert
assessments for Bogs and Fens separately (Table 2), but 87% when considered jointly. This shows that the available
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predictor variables were less suitable for modelling Bogs and Fens separately rather than jointly, which could partly be due
to lower agreement among experts 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 gradients, and can have vegetation
characteristics that make them difficult to distinguish. Models for Bogs and Fens were both strongly influenced by the
histosol distribution in NCS, with secondary influences from the area of “wetlands” in GL30, permafrost extent” in PZI,
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and “mean annual air temperature” in WC2. Predictive models for Marsh and Tundra Wetlands explained less of the
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variability in expert assessments, at 54 and 47%, respectively. The predictive models for Marsh and Tundra Wetlands were
influenced by variables that indicate a transition between terrestrial and aquatic ecosystems, e.g. area of occasional
inundation in GSW, rivers in BAS, and midsize lakes in HL, but then differed in the influence of climate and
permafrost conditions.
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Figure 2. Predicted distribution of wetland classes across the BAWLD domain; a) Bog, b) Fen, c) Marsh, d) Permafrost Bog, and
e) Tundra Wetland.
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Each wetland class had a distinct spatial distribution (Figure 2). Bogs and Fens were the dominant wetland classes in
relatively warmer climates, with high densities in the West Siberian Lowlands, Hudson Bay Lowlands, and the Mackenzie
River Basin. While Bogs and Fens had similarities in their spatial distributions, there was also a relative shift in dominance
from Bogs to Fens in relatively colder and drier climates (Figure S3). These trends are supported by bog to fen transitions
observed both within and between regions (Packalen et al., 2016; Vitt et al., 2000a; Väliranta et al., 2017), but may not be
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universal (Kremenetski et al., 2003). Marshes were also found in warmer climates and largely associated with Bogs and
Fens, but with a more evenly spread distribution. The highest abundance of Marsh coverage was predicted for the Ob River
floodplains, a region with very few studies of CH4 emissions (Terentieva et al., 2019; Glagolev et al., 2011). Bogs, Fens, and
Marshes all decreased in abundance in colder climates, with Permafrost Bogs becoming more abundant than Bogs when
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mean annual temperatures were below -2.5°C, corresponding to findings from western Canada, Fennoscandia, and the West
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Siberian Lowlands (Vitt et al., 2000b; Seppälä, 2011; Terentieva et al., 2016). Tundra Wetlands became dominant over Fens
and Marshes when mean annual air temperatures were below -5.5°C (Figure 3). Tundra Wetlands were predicted to be most
abundant in the lowland regions across the Arctic Ocean coast, with especially high abundance in northern Alaska, eastern
Siberia, and on the Yamal and Gydan Peninsulas in western Siberia.
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Figure 3. Relative abundance of the five wetland classes across a gradient of mean annual temperatures.
We found good agreement between the distribution of wetlands in BAWLD and that of four independent regional spatial
datasets (Figure 4, Figure S4). The best agreements for total wetland cover were between BAWLD and the two datasets
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dedicated specifically to wetland mapping; with R2 of 0.76 with the WSL dataset and 0.72 with the CWI dataset. There were
also strong relationships between BAWLD and the WSL dataset for the distribution of specific wetland classes, for both
drier wetland classes (“ridge”+”ryam”+”palsa” vs. Permafrost Bog + Bog) and wetter classes (“fen” + “hollow” vs. Fen).
When comparing “wet hollow” of the WSL dataset and Marsh in BAWLD there were discrepancies, but they were primarily
attributed to the explicit exclusion of the Ob river floodplains in the WSL dataset (Figure S4). For the wettest classes, we had
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only a weak relationship (R2 = 0.19) between the CWI “Marsh” class and the sum of the BAWLD Marsh and Tundra
Wetland classes, but the overall average abundance for comparable grid cells was similar at 1.4 and 2.2%, respectively.
Agreements between BAWLD and the NLCD and CLC datasets were lower, especially for the relatively drier wetland
classes (Figure S4). Lower agreement between BAWLD and some classes of regional wetland datasets should not be
interpreted to demonstrate poor accuracy of BAWLD, as differences can be due to class definitions, large mapping units, and
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relatively low accuracy of the non-wetland specific regional datasets.
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Figure 4. Comparison of total wetland extent between BAWLD and four regional independent wetland inventories; the National
Land Cover Database (NLCD), the Canadian Wetland Inventory (CWI), the wetland mapping of the West Siberian Lowlands
(WSL), and the CORINE Land Cover dataset (CLC). a) Spatial extents of the regional datasets, b) Correlations between grid cell
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wetland coverages in BAWLD and the regional datasets, c) Spatial distribution of total wetland coverages in the four regional
datasets, d) Spatial distribution of total wetland coverage in BAWLD for grid cells corresponding with the regional datasets.
The 95% confidence intervals for predictions of abundance varied both between wetland classes and among regions (Table
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3, Figure S5, S6). The confidence interval for total wetland area ranged between 2.8 and 3.8 × 106 km2, i.e. a range that
represented 31% of the central estimate. The range of the confidence interval depends both on how much consensus there is
among experts in their assessments, and how well the available spatial datasets used in the random forest modelling can
explain the expert assessments. The considerable range of the confidence interval for wetlands likely stems from a
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combination of these two components. The confidence interval for total area of individual wetland classes varied between
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representing 42% (Fens) and 71% (Marshes) of respective central estimates. The absolute range of confidence intervals for
individual cells generally increased with higher central estimates of abundances, but the range of confidence intervals
decreased if expressed as a percent of the central estimate (Figure S7).
3.2 Lakes
Lakes were predicted to cover a total of 1.44 ×106 km2, or 5.6% of the BAWLD domain. Large Lakes had the greatest lake
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area (44% of total lake area), followed by Midsize Glacial Lakes (26%) and Midsize Peatland Lakes (10%) (Table 3). The
lake classes with the highest CH4 emissions, Small Yedoma Lakes and Small Peatland Lakes, jointly covered 10% of the
total lake area. The total predicted lake area in BAWLD was higher than the area of lakes in HL (1.20 ×106 km2) which only
includes lakes >0.1 km2, and was similar to the area of open water in GL30 (1.43 ×106 km2). The “open water” class in the
GL30 dataset is, however, based on Landsat 30 m resolution data and thus excludes very small open water areas, while it
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includes both lentic and lotic open water. The 95% confidence interval for the total lake area in BAWLD was 0.24 ×106 km2,
or 17% of the central estimate.
The predictive models for the three midsize lake classes each explained between 69 and 75% of the variability in expert
assessments, while a model for the sum of the three midsize lake classes explained 99.1%. The predictive model for the sum
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of the three midsize lake classes was almost exclusively influenced by the area of midsize lakes” in HL, while the three
midsize lake classes were differentiated through further influences by the area of yedoma ground (Midsize Yedoma Lakes),
by the area of histosols and histels in NCS, and wetlands in GL30 (Midsize Peatland Lakes), and by shoreline
length in HL (Midsize Glacial Lakes). The influence of shoreline length for Midsize Glacial Lakes shows that experts
associated glacial lakes with high shoreline development, and low shoreline development with peatland and yedoma lakes.
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Despite similarities in how much of the expert assessments were explained by the predictive models (69-75%), the
extrapolation to the BAWLD domain led to large differences in the 95% confidence interval, which represented only 26% of
the central estimate for Midsize Glacial Lakes, while representing 69 and 140% for Midsize Peatland and Midsize Yedoma
Lakes, respectively (Table 3, Figure S8). Midsize Glacial Lakes were predominately predicted to have high abundances on
the Canadian Shield and in Fennoscandia, while Midsize Yedoma Lakes were associated with the lowland, coastal tundra
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regions of Northeast Siberia and Alaska, and Midsize Peatland Lakes were especially common in the West Siberian
Lowlands, but also common in the peatland regions of the Hudson Bay Lowlands and the Mackenzie River Basin and in
coastal lowland regions (Figure 5).
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Figure 5. Predicted distributions of lake and river classes within the BAWLD domain; a) Large Lakes, b) Midsize Glacial Lakes, c)
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Midsize Peatland Lakes, d) Midsize Yedoma Lakes, e) Small Glacial Lakes, f) Small Peatland Lakes, g) Small Yedoma Lakes. h)
Large Rivers, i) Small Organic-Rich Rivers, j) Small Organic-Poor Rivers.
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Small Glacial, Yedoma, and Peatland Lakes were jointly estimated to cover 0.9% of the BAWLD domain. The predictive
models explained 16, 39, and 66% of the variability, respectively (Table 2). The relatively lower predictive power for small
lakes was not unexpected, given lack of information on the smallest open water systems in the available spatial data, the
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variable abundance of very small open water systems among landscapes (Muster et al., 2019), and a lower relative consensus
among experts when assessing classes with generally small fractional coverages. Models for all three small lake classes were
influenced by the area of “occasional inundation” in GSW but were then differentiated by variables largely similar to those
that were characteristic of the corresponding midsize lake classes (Table 2). The predicted distributions of the small lake
classes were also largely similar to that of the corresponding midsize lake type classes (Figure 5). The overall predicted area
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of small lakes was 0.24 ×106 km2, representing 17% of the total lake area. The combined 95% uncertainty for the three
classes ranged between 0.15 and 0.38 ×106 km2 (Table 3, Figure S8), suggesting that small lakes represent between 11 and
26% of the total lake area. Previous assessments have estimated that open water ecosystems <0.1 km2 represent between 21
and 31% of global lake area (Holgerson and Raymond, 2016), but relied on assumptions in the statistical modelling which
may lead to bias for boreal and arctic regions (Cael and Seekell, 2016; Muster et al., 2019).
595
3.3 Rivers
Rivers were predicted to cover a total of 0.12 ×106 km2, or 0.47% of the BAWLD domain. Large Rivers accounted for 65%
of the total river area in BAWLD. These estimates were similar to global assessments, where streams and rivers have been
estimated to cover between 0.30 and 0.56% of the land area, with 65% of the river area consisting of large rivers of 6th or
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greater stream order (Downing et al., 2012). The predictive model for Large Rivers was strongly influenced by the area of
“large rivers” in GLWD, but experts consistently made lower assessments which led to an overall 15% lower area of Large
Rivers compared to the area of rivers in GLWD within the BAWLD domain.
Small Organic-Poor and Small Organic-Rich Rivers were estimated to represent 27% and 8%, respectively, of the total river
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area. The predictive models for the Small Organic-Poor and Organic-Rich Rivers explained 19 and 59% of the expert
assessments, and were distinctly influenced by the area of “occasional inundation” in GSW and “wetlands” in GLC30,
respectively. The estimated area of small rivers varied among experts, reflecting difficulties in consistent assessments among
experts for land cover classes with low extents (<1% in most grid cells). The distributions of expert assessments for small
river areas were non-normal, leading to a long upper tail for the 95% confidence interval (Figure S9). For example, the low,
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central, and high estimates for the area of Small Organic Rich Rivers were 0.005, 0.10 and 0.54 ×106 km2, respectively. The
predicted distributions showed that Small Organic-Rich Rivers was closely associated with the distribution of the BAWLD
wetland classes, while Small Organic-Poor Rivers dominated elsewhere, with especially high abundances in regions with
higher mean annual precipitation (Figure 5).
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3.4 Other Classes
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Boreal Forest, Dry Tundra, Rocklands, and Glaciers were predicted to cover 10.7, 5.3, 2.7 and 2.1 ×106 km2, respectively,
within the BAWLD domain (Figure S10). The predictive models explained between 96% (Glaciers) and 67% (Rocklands) of
the variability in expert assessments. While the predictive models for Glaciers was almost exclusively influenced by the area
of “permanent snow and ice” in GL30, several variables influenced predictions of Rocklands including area of “rocklands”
in NCS, “mountainous” and “rugged” terrain in PZI, and “barrens” in CAVM. The predictive models for Boreal Forest and
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Tundra suggested that the transition between these classes was strongly influenced by the area “forest” in GLC2, and by the
distinction between “tundra” and “boreal” terrestrial ecoregions in TEW.
3.5 Wetscapes
We defined wetscapes as regions with characteristic composition of specific wetland, lake, and river classes. Our
clustering analysis distinguished 15 typical wetscapes within the BAWLD domain (Figure 6), each defined by the relative
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presence or absence of the 19 BAWLD classes (Table S1). Visualising the distribution of wetscapes provides information on
regions that are likely to have similarities in the magnitude, seasonality, and climatic controls over CH4 emissions.
Three wetscapes common in boreal regions were differentiated based on the abundance of non-permafrost wetlands. The
Sparse, Common, and Dominant Boreal Wetlands wetscapes all had limited lake coverage (<6% on average), but had 15, 35,
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and 60% combined coverages of Bogs, Fens, and Marshes, respectively. The Dominant Boreal Wetlands wetscape was
almost exclusive to the non-permafrost regions of the Hudson Bay Lowlands and the West Siberian Lowlands. The Common
Boreal Wetlands wetscape was more widespread, found adjacent to the core areas of the Hudson Bay Lowlands and the West
Siberian Lowlands, but also in the Mackenzie River Basin, northern Finland, European Russia, and in the Kamchatka
Lowlands. The Sparse Boreal Wetlands wetscape was widespread in Sweden, Finland, European Russia, and the southern
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boreal regions of Canada outside of Yukon. Emissions of CH4 from these regions are likely dominated by wetlands rather
than lakes, with main sensitivity to climate change being altered water balance (Tarnocai, 2006; Olefeldt et al., 2017; Olson
et al., 2013).
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Figure 6. Wetscapes of the Boreal-Arctic Wetland and Lake Dataset. Wetscapes are defined by their characteristic composition of
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the BAWLD land cover classes, and thus groups regions with similar abundances (or absences) of specific wetland, lake, and river
classes. The 15 wetscapes have their average land cover composition indicated by pie charts, with the legend shown in the bottom
left. For clarity, the small and mid-sized lakes classes were combined for glacial, peatland, and yedoma lakes, and the river classes
were omitted from the pie charts. No land cover pie charts are shown for the Large Lakes, Rivers, and Glaciers wetscapes.
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The Lake-rich Peatlands and the Permafrost Peatlands wetscapes were both found in lowland regions with discontinuous
permafrost, near the boreal to tundra transition. The Lake-rich Peatlands wetscape was almost exclusively found in the West
Siberian Lowlands, north of the Ob River. This wetscape was characterized by roughly equal abundances of Bogs, Fens and
Permafrost Bogs (each 14-16%), along with 8% Marshes, 9% Small Peatland Lakes and 5% Midsize Peatland Lakes. It is
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notable that this wetscape, with the highest coverages of high-CH4 emitting marshes and peatland lakes, has no presence in
North America. The Permafrost Peatlands wetscape was conversely primarily found in the Hudson Bay Lowlands and the
Mackenzie River Basin, with additional coverage along the Arctic Ocean coast in European Russia, in interior Alaska, and in
the Anadyr Lowlands of far eastern Russia. This wetscape had the greatest abundance of Permafrost Bogs (27%), with less
contribution from other wetland classes (16%), and relatively low abundance of lakes (7%). The Lake-rich Peatlands
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wetscape likely has the highest regional CH4 emissions, while the Permafrost Peatlands wetscape likely has low to moderate
emissions. However, CH4 emissions from both these wetscapes are likely highly sensitive to climate change due to the rapid
ongoing and future permafrost thaw that causes expansion of thermokarst lakes and non-permafrost wetlands at the expense
of Permafrost Bogs (Bäckstrand et al., 2008; Turetsky et al., 2002).
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Three wetscapes were found in lowland tundra regions, and varied in relative dominance of different wetland and lake
classes. Wetland-rich Tundra had 23% wetlands but only 7% lakes, and was found on the Gydan and Taymyr peninsulas in
North Siberia, with minor extents in far eastern Siberia and in Alaska. Wetland and Lake-rich Tundra had similar wetland
cover (24%) but twice the coverage of lakes (15%), split equally between glacial and peatland lakes. It was found on the
Alaska North Slope along with minor extents on the Yamal Peninsula, the Mackenzie River Delta, and on sections of Baffin
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Island. Lastly, the Wetland and Lake-rich Yedoma Tundra was characterized by the highest abundance of yedoma lakes
(8%), a total wetland and lake coverage of 46%, and was primarily found in the Kolyma Lowlands, with minor extents in the
Yukon-Kuskokwim Delta and on the Alaska North Slope. These regions may have sensitive CH4 emissions, particularly
associated with thermokarst lake expansion where highly labile yedoma sediments fuel high CH4 production (Walter
Anthony et al., 2016).
670
The remaining seven wetscapes are likely to have overall low CH4 emissions, or even net uptake, as a result either from the
dominance of low-CH4 emitting classes or due to the relative absence of wetland and lake classes. The Dry Tundra wetscape
was common in regions of undulating topography of northernmost Siberia, the Alaska North Slope, and the western
Canadian Arctic, and was characterized by relatively low abundances of wetlands (9%) and lakes (3%). The Lake-rich Shield
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wetscape was exclusive to the Canadian 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 Boreal wetscape dominates boreal
regions of Siberia but is also found in the Yukon, Alaska, and Quebec, and was defined by having <5% wetlands and 0.5%
lakes. The Alpine and Tundra Barrens wetscape had <2% wetlands and ~1.5% lakes, and dominates the Greenland coast, the
high-latitude polar deserts of the Canadian Arctic Archipelago, and the mountain ranges in Fennoscandia, Alaska, Yukon,
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and eastern Siberia. Lastly, the Glaciers, Large Lakes, and Large Rivers wetscapes were defined by the dominance of the
namesake BAWLD classes.
5 Data Availability
The fractional land cover estimates from the Boreal-Arctic Wetland and Lake Dataset (BAWLD) is freely available at the
Arctic Data Center (Olefeldt et al., 2021): https://doi.org/10.18739/A2C824F9X. The dataset is provided as an ESRI
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shapefile (.shp) and as a Keyhole Markup Language (.kml) file.
6 Conclusions
The Boreal-Arctic Wetland and Lake Dataset (BAWLD) was developed to provide improved estimates of areal extents of
five wetland classes, seven lentic ecosystem classes, and three lotic ecosystem classes by leveraging expert knowledge along
with available spatial data. By differentiating between wetland, lake and river classes with distinct characteristics, BAWLD
690
will be suitable to support large-scale modelling of high-latitude hydrological and biogeochemical impacts of climate
change. In particular, BAWLD has been developed with the aim to facilitate improved modelling of current and future CH4
emissions. For example, a dataset of empirical CH4 data was co-developed with BAWLD (Kuhn et al., 2021), ensuring that
the land cover classification was meaningful for the separation of classes based on distinct magnitudes and controls of CH4
emissions. By being based on an expert assessment and existing spatial dataset rather than a remote sensing approach,
695
BAWLD was able to provide predictions for abundance of high-CH4 emitting wetland and lake classes that have limited
extents but disproportionate influences on regional and overall CH4 emission. Using BAWLD for upscaling of CH4
emissions will reduce issues of representativeness of empirical data for upscaling, reduce the risk of overlap between
wetland and lake classes, and allow for more rigorous uncertainty analysis.
Author contributions
700
This study was conceived by DO. The GIS work was done by MH. The information sent to experts to complete the expert
assessment was compiled by DO, MH, and MAK. All co-authors completed the expert assessment. The random forest
modelling was led by DO, with input from TB, AR, and MJL. Data analysis and visualizations was led by DO with input
from all co-authors. The manuscript was written by DO with contributions from all co-authors.
Competing Interests
705
The authors declare no competing interests.
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Acknowledgements
Financial support to DO was provided the National Science and Engineering Research Council of Canada (NSERC)
Discovery grant (RGPIN-2016-04688) and the Campus Alberta Innovates Program. CT was supported by ERC (#851181)
and the Helmholtz Impulse and Networking Fund. AM was supported by the Gordon and Betty Moore Foundation (Grant
710
GBMF5439, 839; Stanford University). DB was supported by ERC (#725546), Swedish Research Council VR (#2016-
04829), and FORMAS (#2018-01794). FJWP was supported by the Norwegian Research Council under grant agreement
274711, and the Swedish Research Council under registration no. 2017-05268. GG was supported through the BMBF KoPf
Synthesis project (03F0834B). JDW was supported by NASA Earth Science (NNH17ZDA001N). MJL was supported by
NSF-EnvE (#1928048). MS was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC)
715
through the Canada Research Chairs program. RKV was supported by the National Aeronautics and Space Administration
IDS program (NASA grant NNX17AK10G). SAF was supported by the Natural Sciences and Engineering Research Council
of Canada. SET was supported by funding from the Campus Alberta Innovates Program. Ducks Unlimited Canada’s
Wetland Inventories were funded by various partnering organizations: Environment and Climate Change Canada, Canadian
Space Agency, Government of Alberta, Government of Saskatchewan, U.S. Forest Service, U.S. Fish and Wildlife Service,
720
PEW Charitable Trusts, Canadian Boreal Initiative, Alberta-Pacific Forest Industries Inc., Mistik Management Ltd.,
Louisiana-Pacific, Forest Products Association of Canada, Weyerhaeuser, Lakeland Industry and Community, Encana,
Imperial Oil, Devon Energy Corporation, Shell Canada Energy, Suncor Foundation, Treaty 8 Tribal Corporation
(“Akaitcho”), and Dehcho First Nations. The Permafrost Carbon Network provided coordination support, and is funded by
the NSF PLR Arctic System Science Research Networking Activities (RNA) Permafrost Carbon Network: Synthesizing Flux
725
Observations for Benchmarking Model Projections of Permafrost Carbon Exchange, Grant # 1931333 (2019-2023).
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... Five lakes were monitored for seasonal greenhouse gas emission at each study location, indicated by orange circles. The distribution of peatlands within both the study region and the broader circumpolar region is shown in the top right inset, showing that the study region is one of the major northern peatland regions (Olefeldt et al., 2021). Bottom right photos show one lake from each study location, with all lakes surrounded by treed peatlands. ...
... We estimated current lake CH 4 and CO 2 emissions from the Taiga Plains by multiplying the area of small and midsized peatland lakes from the Boreal Arctic Wetland and Lake Databset ("BAWLD"; Olefeldt et al., 2021) by the mean CH 4 and CO 2 flux specific to the MAAT reported for each 0.5 × 0.5° grid cell of the database and the number of ice-free days. Daytime diffusive CH 4 emissions were multiplied by a diel correction factor (0.70) to account for diel patterns in CH 4 emissions between day and night (i.e., "24-hr fluxes"; Sieczko et al., 2020). ...
... Thus, our results may under-estimate net radiative forcing for both CH 4 and CO 2 , however, our study design does take into account emissions from moderately impacted thermokarst edges which were the most common thermokarst feature across the study lakes. Peatland lakes <10 km 2 in size cover ∼260,000 km 2 within the Taiga Plains (Olefeldt et al., 2021) and were estimated to emit 0.20 ± 0.08 Tg CO 2 -C y −1 and 0.048 ± 0.01 Tg CH 4 -C y −1 under the current climate. Emissions of CO 2 were estimated to decrease, on average, by 16.5% and 68% under the RCP 2.6 and 4.5 scenarios, respectively (Figure 6b), while CH 4 emissions increased by 31% and 121%, respectively (Figure 6b). ...
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... Lakes, through their seasonal ice cover extent and duration, are a major landscape in the Arctic [1][2][3][4][5]. Governed by both geographical and morphological settings, the spring lake ice phenology (i.e., timing of ice-off) is a primary driver of Arctic hydrological processes [6], and has been identified as a sensitive metric to measure climate change [7][8][9]. ...
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The timing of lake ice-off regulates biotic and abiotic processes in Arctic ecosystems. Due to the coarse spatial and temporal resolution of available satellite data, previous studies mainly focused on lake-scale investigations of melting/freezing, hindering the detection of subtle patterns within heterogeneous landscapes. To fill this knowledge gap, we developed a new approach for fine-resolution mapping of Pan-Arctic lake ice-off phenology. Using the Scene Classification Layer data derived from dense Sentinel-2 time series images, we estimated the pixel-by-pixel ice break-up end date information by seeking the transition time point when the pixel is completely free of ice. Applying this approach on the Google Earth Engine platform, we mapped the spatial distribution of the break-up end date for 45,532 lakes across the entire Arctic (except for Greenland) for the year 2019. The evaluation results suggested that our estimations matched well with both in situ measurements and an existing lake ice phenology product. Based on the generated map, we estimated that the average break-up end time of Pan-Arctic lakes is 172 ± 13.4 (measured in day of year) for the year 2019. The mapped lake ice-off phenology exhibits a latitudinal gradient, with a linear slope of 1.02 days per degree from 55°N onward. We also demonstrated the importance of lake and landscape characteristics in affecting spring lake ice melting. The proposed approach offers new possibilities for monitoring the seasonal Arctic lake ice freeze–thaw cycle, benefiting the ongoing efforts of combating and adapting to climate change.
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Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal–Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5∘ 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 methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ∼ 28 % each of the total wetland area, while the highest-methane-emitting marsh and tundra wetland classes occupied 5 % and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 % and 4 %, respectively. Small (
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Nearly 25% of all lakes on earth are located at high latitudes. These lakes are formed by a combination of thermokarst, glacial, and geological processes. Evidence suggests that the origin of periglacial lake formation may be an important factor controlling the likelihood of lakes to drain. However, geospatial data regarding the spatial distribution of these dominant Arctic and subarctic lakes are limited or do not exist. Here, we use lake-specific morphological properties using the Arctic Digital Elevation Model (DEM) and Landsat imagery to develop a Thermokarst lake Settlement Index (TSI), which was used in combination with available geospatial datasets of glacier history and yedoma permafrost extent to classify Arctic and subarctic lakes into Thermokarst (non-yedoma), Yedoma, Glacial, and Maar lakes, respectively. This lake origin dataset was used to evaluate the influence of lake origin on drainage between 1985 and 2019 in northern Alaska. The lake origin map and lake drainage datasets were synthesized using five-year seamless Landsat ETM+ and OLI image composites. Nearly 35,000 lakes and their properties were characterized from Landsat mosaics using an object-based image analysis. Results indicate that the pattern of lake drainage varied by lake origin, and the proportion of lakes that completely drained (i.e., >60% area loss) between 1985 and 2019 in Thermokarst (non-yedoma), Yedoma, Glacial, and Maar lakes were 12.1, 9.5, 8.7, and 0.0%, respectively. The lakes most vulnerable to draining were small thermokarst (non-yedoma) lakes (12.7%) and large yedoma lakes (12.5%), while the most resilient were large and medium-sized glacial lakes (4.9 and 4.1%) and Maar lakes (0.0%). This analysis provides a simple remote sensing approach to estimate the spatial distribution of dominant lake origins across variable physiography and surficial geology, useful for discriminating between vulnerable versus resilient Arctic and subarctic lakes that are likely to change in warmer and wetter climates.
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Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.
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Plain Language Summary Large wedge‐shaped masses of ice commonly occur in Arctic tundra just below the ground surface. These ice wedges form polygon patterns observable by aircraft and remote sensing. Warming temperatures are causing ice wedges to melt, forming troughs that widen, deepen, and flood over time to become small ponds. Some ice wedges reform over decades as troughs become drier and seasonal frost persists belowground. We conducted field measurements to quantify how melting and regrowing ice wedges affect the exchange of greenhouse gases carbon dioxide (CO2) and methane (CH4) between ground and pond surfaces and Earth's atmosphere in an area of Arctic Alaska where ice wedges are melting, and some are regrowing. Then we combined field measurement results with study area aerial photos from nine different years during 1949–2018 to assess how ice wedge distribution and greenhouse gas exchange has changed over the past seven decades. CH4 emissions to the atmosphere increased over time as ice wedges melted, but at the same time, more CO2 was taken out of the atmosphere by increased localized plant growth, counteracting some of the warming effect of CH4. The balance of these simultaneous changes indicates a net potential of melting ice wedges to add to a warming climate.
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The expansion of shrubs across the Arctic tundra may fundamentally modify land‐atmosphere interactions. However, it remains unclear how shrub expansion pattern is linked with key environmental drivers, such as climate change and fire disturbance. Here we used 40+ years of high‐resolution (~1.0 m) aerial and satellite imagery to estimate shrub‐cover change in 114 study sites across four burned and unburned upland (ice‐poor) and lowland (ice‐rich) tundra ecosystems in northern Alaska. Validated with data from four additional upland and lowland tundra fires, our results reveal that summer precipitation was the most important climatic driver (r = 0.67, p < 0.001), responsible for 30.8% of shrub expansion in the upland tundra between 1971 and 2016. Shrub expansion in the uplands was largely enhanced by wildfire (p < 0.001) and it exhibited positive correlation with fire severity (r = 0.83, p < 0.001). Three decades after fire disturbance, the upland shrub cover increased by 1077.2 ± 83.6 m2 ha‐1, ~7 times the amount identified in adjacent unburned upland tundra (155.1 ± 55.4 m2 ha‐1). In contrast, shrub cover markedly decreased in lowland tundra after fire disturbance, which triggered thermokarst‐associated water impounding and resulted in 52.4% loss of shrub cover over three decades. No correlation was found between lowland shrub cover with fire severity (r = 0.01). Mean summer air temperature (MSAT) was the principal factor driving lowland shrub‐cover dynamics between 1951 and 2007. Warmer MSAT facilitated shrub expansion in unburned lowlands (r = 0.78, p < 0.001), but accelerated shrub‐cover losses in burned lowlands (r = – 0.82, p < 0.001). These results highlight divergent pathways of shrub‐cover responses to fire disturbance and climate change, depending on near‐surface permafrost and drainage conditions. Our study offers new insights into the land‐atmosphere interactions as climate warming and burning intensify in high latitudes.
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The northern mid- to high-latitudes have the highest total number and area of lakes on Earth. Lake origins in these regions are diverse, but to a large extent coupled to glacial, permafrost, and peatland histories. The synthesis of 1207 northern lake initiation records presented here provides an analog for rapid landscape-level change in response to climate warming, and its subsequent attenuation by physical and biological feedback mechanisms. Our compilation reveals two peaks in northern lake formation, 13,200 and 10,400 years ago, both following rapid increases in North Atlantic air temperature. Placing our findings within the context of existing paleoenvironmental records, we suggest that solar insolation-driven changes in climate (temperature and water balance) that led to deglaciation and permafrost thaw likely contributed to high rates of northern lake formation during the last Deglacial period. However, further landscape development and stabilization dramatically reduced rates of lake formation beginning ∼10,000 years ago. This suggests that temperature alone may not control future lake development; rather, multiple factors must align to enable a landscape to respond with an increase in lake area. We propose that land surfaces strongly geared toward increased lake formation were highly conditioned by glaciation. Thus, it is unlikely that warming this century will cause lake formation as rapid or as widespread as that during the last Deglacial period.
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Greenhouse gas emissions from physical permafrost thaw disturbance and subsidence, including the formation and expansion of thermokarst (thaw) lakes, may double the magnitude of the permafrost carbon feedback this century. These processes are not accounted for in current global climate models. Thermokarst lakes, in particular, have been shown to be hotspots for emissions of methane (CH4), a potent greenhouse gas with 32 times more global warming potential than carbon dioxide (CO2) over a 100-year timescale. Here, we synthesize several studies examining CH4 dynamics in a representative first-generation thermokarst lake (Vault Lake, informal name) to show that CH4 production and oxidation potentials vary with depth in thawed sediments beneath the lake. This variation leads to depth-dependent differences in both in situ dissolved CO2:CH4 ratios and net CH4 production responses to additional warming. Comparing CH4 production, oxidation, and flux values from studies at Vault Lake suggests up to 99% of produced CH4 is oxidized and/or periodically entrapped before entering the atmosphere. We summarize these findings in the context of CH4 literature from thermokarst lakes and identify future research directions for incorporating thermokarst lake CH4 dynamics into estimates of the permafrost carbon feedback.