Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

. Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH 4 ) emissions. Current maps of global wetland extent vary in their wetland definition, causing substantial disagreement 25 and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH 4 modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) version 1.0 dataset at ~25 km resolution at equator (0.25º) at monthly time-step for 2000-2018. WAD2M minimum), and intermittently inundated wetlands at 5.5 Mkm 2 (long-term maximum minus mean annual maximum). WAD2M shows good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with Cohen’s kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will 40 improve estimates of wetland CH 4 fluxes for global-scale land surface modeling. The dataset can be found at http://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).


Introduction
Wetlands cover about 10% of global land area (Davidson et al., 2018) and play an important role in regulating global climate via biogeochemical cycling of greenhouse gases (IPCC, 2013). Wetlands are highly productive ecosystems that store large 45 amounts of soil carbon due to their waterlogged conditions inhibiting aerobic soil respiration. Flooded conditions alter the soil redox state for microbes to favor methanogenesis and thus wetlands are the largest natural source of methane (CH4) to the atmosphere, contributing ~20-30% of the total annual global methane budget (Kirschke et al., 2013;Saunois et al., 2016Saunois et al., , 2020. The spatial and temporal distribution of wetlands is one of the most important and yet uncertain factors determining the time and location of CH4 fluxes (Melton et al., 2013;Parker et al., 2018). Wetlands are at risk from human activities such 50 as land clearing and drainage, and also at risk from climate change caused drying or less predictable precipitation events (Davidson et al., 2018).
Because wetland definitions vary between science, applications and policy objectives, a definition suitable for CH4 modeling is needed for comparative reasons and to avoid double counting. Since the first global wetland map of Matthews and Fung (Matthews and Fung, 1987), several additional global and regional wetland area datasets have been developed (Table A1).

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These datasets are characterized by differences in definition, data sources, methodologies and time period covered. For example, the Ramsar Convention on Wetlands focusing on waterfowl conservation defines wetlands as both vegetated and non-vegetated systems (i.e., rivers, lakes, ponds). However, the biogeochemistry and methane flux pathways from open water and vegetated wetlands differs substantially. Additionally, human-made water bodies (e.g. reservoirs, rice paddies, agricultural wastewater ponds (i.e., aquaculture (Grinham et al., 2018)) are considered wetlands in the definition of the IPCC 60 National Greenhouse Gas Inventory guidelines (Hiraishi et al., 2014). The biogeochemical processes in these kinds of intensely managed wetlands differ from those of natural wetlands, and generic modelling approaches are not applicable.
Boreal taiga forests and tropical floodplains, which are considered CH4-emitting areas given their seasonally inundated states and significant CH4 transport pathway via tree stem (Barba et al., 2019;Pangala et al., 2017), are omitted from many wetland mapping products due to the difficulty in detecting dense forest canopies that hide surface inundation.
Characterizing the seasonal and interannual variation in wetland extent is critical to improving global-scale wetland CH4 75 modeling. Contemporary evidence from remote sensing (Alsdorf et al., 2000(Alsdorf et al., , 2007Hu et al., 2018;Lunt et al., 2019;Melack et al., 2004;Pandey et al., 2020;Prigent et al., 2007Prigent et al., , 2012Rodell et al., 2018) and field monitoring (Dunne and Aalto, 2013) suggest that global wetlands, especially tropical floodplains, have a significant seasonal cycle and interannual variability in spatial extent that depend on changes in water balance (i.e. precipitation, runoff, and evapotranspiration) and local topography. Despite the critical importance of spatial and temporal changes in wetland area, there are large 80 discrepancies among the estimates of global wetland extent Melton et al., 2013;Pham-Duc et al., 2017;Wania et al., 2013) and only a limited number of available global products characterize temporal dynamics in wetland extent (Gallant, 2015;Huang et al., 2014;Prigent et al., 2007Prigent et al., , 2020. Remotely sensed observations show potential for capturing spatio-temporal wetland patterns. While bottom-up inventories define wetlands based on a combination of soils, hydrology and vegetation, satellite-based observations of surface 85 inundation (i.e. water above the soil) capture areas that are permanently or seasonally wet. Microwave sensor-based products (Jensen and McDonald, 2019;Papa et al., 2010;Prigent et al., 2020;Schroeder et al., 2015) can sense water below vegetated canopies and now provide a multi-decadal records, with weekly-to-monthly revisit times. Optical sensor-based products using visible or infrared bands (Amani et al., 2019;Feng et al., 2016;Jones, 2019;Pekel et al., 2016;Wulder et al., 2018;Yamazaki et al., 2015) observe the open water dynamics but have limited capacity to detect surface water beneath vegetation 90 canopy. L-band (~1 GHz) synthetic aperture radar (SAR) can detect flooding beneath most vegetation canopies and is more successful at mapping forested wetlands than higher frequency observations such as optical or microwave products. These products separate inland water types at a high spatial resolution, but typically provide limited temporal coverage.
Data fusion approaches that merge remote sensing observations from multiple sources of sensors at different spatial resolutions presents a feasible way to properly capture the dynamics of wetland extent. Despite recent progress in wetland 95 mapping, long-term wetland dynamic datasets specifically suited for global CH4 studies (Poulter et al., 2017) is an area of active research. Further, recent work has shown significant differences between remote sensing wetland products (Pham-Duc et al., 2017). These discrepancies can be linked to methodological differences (including pre-processing), data sources, and definitions. This introduces large biases in the modeling of wetland CH4 emissions (Bohn et al., 2015), that can be traced to https://doi.org/10.5194/essd-2020-262 the following limitations: 1) higher-spatial resolution optical sensors can only detect open water in the absence of clouds and 100 vegetation (while SAR measurements can penetrate cloud and dense canopies but have inconsistent temporal coverage at the required wavelength); 2) available coarse-spatial resolution microwave based products accurately represent surface water only under low vegetation canopy cover conditions; 3) the intrinsic limitations in remote sensing include the difficulty in detecting inundation under snow cover. In addition, several recent studies (Fluet-Chouinard et al., 2015;Hess et al., 2015;Prigent et al., 2007;Reschke et al., 2012) suggests that the wetland mapping products at coarse resolution tend to overlook 105 small inundated areas. Some of the difficulty in merging these products arises from ambiguity in definitions of inundated versus open water wetlands. Also, widely used descriptions of wetlands (shallow water with depth less than 2-2.5m (Cowardin et al., 1979;Tiner et al., 2015)) overlap with a vast array of lakes and small ponds -especially in permafrost peatlands and thermokarst regions (West and Plug, 2008). The confusion between wetlands and waterbodies risks doublecounting CH4 emissions from high-latitudes (Thornton et al., 2016). All these issues lead to biases and uncertainties in 110 developing a global dataset of wetland extent.
The objective of this study is to develop a global dynamic wetland dataset with a data fusion approach using consistent definitions for use in wetland methane emission studies. Given the many wetland types used in the literature, we chose an operational definition of wetlands as all natural vegetated forested and non-forested wetlands, excluding coastal wetlands, cultivated wetlands such as irrigated rice paddies, and open water systems such as rivers, streams, lakes, ponds, and 115 reservoirs. Estimates of the methane producing area are used in all bottom-up CH4-flux methodologies: from upscaling fluxes measured by eddy covariance at ecosystem scale (Knox et al., 2019;Peltola et al., 2019;Treat et al., 2018) to processbased modeling at global scale (Bloom et al., 2010;Melton et al., 2013;Poulter et al., 2017).
The resulting dataset, named Wetland Area Dataset for Methane Modelling (WAD2M), is designed to fuse multiple datasets including ground-based wetland inventories, remote sensing products of open waters and surface inundation dataset based on 120 optical and active and passive microwave satellite observations. Within this framework, the Surface Water Microwave Products Series (SWAMPS) is used as the basis for providing the temporal dynamics at a monthly timestep and at a spatial resolution of 0.25° over a 19-year period (2000-2018). A set of wetland-related datasets at different spatial resolutions representing lakes, ponds, rivers and streams, rice paddies, and a coastal mask, are applied to filter out non-vegetated and anthropogenic wetlands. Another set of static maps representing non-inundated wetlands, such as peatlands, are used to fill-125 in the gaps of SWAMPS. Uncertainties are derived by comparing WAD2M with available benchmark products at regional and global scales.

Overview of data processing and wetland definition
Our data fusion approach begins with a time series of global, monthly surface inundation provided by SWAMPS v3.2 130 (Jensen and McDonald, 2019). The SWAMPS data set is derived from a series of active and passive microwave remote sensing observations used to estimate total area of surface inundation including all natural and managed terrestrial (open-to closed canopy vegetation) and open-water bodies, including coastal, lakes, rivers, ponds. All ancillary datasets (inventoried wetlands, remotely-sensed inland waters, rice, ocean) were re-gridded to 0.25-degree resolution to match SWAMPS and expressed as fractional areas. The following sections describe the data processing in the following steps ( Figure 1): The 135 SWAMPS dataset was used to represent the temporal variation in wetland dynamics. For the wetland regions that were not captured or well-represented in SWAMPS mainly due to closed-canopy conditions, independent datasets of static wetland distributions were fused with SWAMPS. The merger was carried out in five steps: 1) by calculating the long-term maximum annual surface inundation from SWAMPS (fwmax), 2) on a per-pixel basis comparing fwmax with the independent datasets of static wetland distributions (see Methods 2.2), 3) adjusting fwmax to match the wetland maps for pixels where fwmax is less 140 than the static distribution, 4) imposing the SWAMPS seasonal cycle to the corrected fwmax dataset, and 5) removing inland water bodies, coastal waters, and rice agriculture.
We added missing wetlands to SWAMPS by fusing it with best available maps and inventories of under-represented wetlands separately across three latitudinal bands. For northern wetland inventories, we used the Northern Circumpolar Soil Carbon Dataset (NCSCD; (Hugelius et al., 2013) to map permafrost and non-permafrost peatlands (Histels and Histosols).

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Mineral soil wetlands were mapped from SAR-based map by including occurrences of wetlands in the circum-arctic (Widhalm et al., 2015) outside areas mapped as peatlands by the NCSCD. In the tropics, we used a 231-m resolution pantropical dataset based on geomorphic classification approach (Gumbricht et al., 2017). For temperate regions not covered by either the boreal and tropical datasets, we used the 1-km Global Lakes and Wetlands Dataset (GLWD) Level 3 after removing Classes 1-3 lakes and rivers (Lehner and Döll, 2004). The global dataset of Monthly Irrigated and Rainfed Crop 150 Areas (MIRCA2000) at 10-km resolution, was used to remove rice agriculture (Portmann et al., 2010). Lakes, ponds, rivers and other permanent inland water bodies were removed using the Landsat Global Surface Water dataset (Pekel et al., 2016).
An ocean/coastline mask based on MOD44W Collection 6 (Carroll et al., 2009), a 250-m resolution annual product from the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensor, was used to remove ocean waters. The new SWAMPS v3.2 (Jensen and McDonald, 2019), is an updated version over SWAMPS v2.0 (Schroeder et al., 2015) that was 155 used as input in the hybrid wetland product SWAMPS-GLWD (Poulter et al., 2017), the predecessor of WAD2M. The major differences between WAD2M and SWAMPS-GLWD are that 1) WAD2M uses an updated version SWAMPS v3.2 with improved algorithm and ancillary datasets; 2) WAD2M uses multiple static wetland maps as mergers in the processing (while SWAMPS-GWLD only considers GLWD in the processing); 3) The WAD2M includes removal of lakes, ponds, rivers, streams and irrigated rice paddies, and 4) WAD2M uses a globally consistent ocean/land mask. To characterize the temporal dynamics, three wetland statistics were computed: (1) Mean Annual minimum (MAmin); (2) Mean Annual maximum (MAmax); (3) Long-term Annual Maximum (MALt). For each 0.25-degree grid cell, annual magnitude in wetland area can be calculated as difference between MAmax and MAmin, while wetland areas that do not flood during the average year (i.e., intermittent wetlands) can be calculated as difference between MALt and MAmin.

Wetland Dynamic Dataset
The Surface Water Microwave Product Series v3.2 (SWAMPS) is a long-term, daily time series of inundated area fraction dataset derived from microwave remote sensing. The SWAMPS dataset provides estimates of terrestrial surface water dynamics, including for wetlands, rivers, lakes, ponds, reservoirs, rice paddies, and episodically inundated areas. SWAMPS provides estimates of global inundated area fraction (fw) developed under the NASA Making Earth System Data Records for 170 Use in Research Environments Program (MEASURES). SWAMPS fw estimates are derived from a combination of passive microwave brightness temperature and active microwave radar backscatter from a variety of satellite sensors supplemented with a priori knowledge of land cover based on a static MODIS land cover product (Schroeder et al., 2015). The sensors used in SWAMPS product include daily gridded DMSP Special Sensor Microwave Imager-Special Sensor Microwave Imager Sounders (SSMI-SSMIS) Pathfinder brightness temperature observations and active microwave backscatter from 175 NASA SeaWinds-on-QuikSCAT Level 1B Sigma0 Product and Advanced Scatterometer Level 1B (ASCAT) product, with ancillary snow water equivalent, land cover map and NDVI from AVHRR and MODIS for delineating snow cover and arid and semiarid areas. SWAMPS v3.2 is an update of v2.0 and includes a new cloud and snow mask, a quality control flag, a new land and ocean mask, freeze-thaw detection, and improved sensor intercalibration. For the purpose of this study, the SWAMPS v3.2 dataset, covering the years 2000 to 2018, were merged into a single time series using samples flagged as 180 'Valid Observations'. For SWAMPS v3.2, the coastal zone was filtered out using a Landsat-based 90-m mask of permanent ocean waters defined by the G3WBM Global Water Body Map dataset (Yamazaki et al., 2015) but later re-filtered using the MODIS MOD44W product. The SWAMPS v3.2 data were remapped to WGS84 using bilinear interpolation at 0.25degree resolution with values aggregated from daily to monthly means.

Open water & land-ocean masks 185
The Global Surface Water (GSW) product is derived from 16-day Landsat thematic mapper imagery at 30-m spatial resolution and identifies the presence or absence of water bodies over the period 1984-2016 (Pekel et al., 2016). We used this dataset to represent permanent water bodies which we define as those covered by open water for more than 50% of the months during this time period. We used this as a permanent waterbody mask to avoid including temporary waterbodies that are considered wetlands in our working definition. This distribution of long-term maximum permanent water was re-gridded 190 to 0.25-degree fractional area per grid cell and used for removing inland-water areas from SWAMPS v3.2. Because the https://doi.org/10.5194/essd-2020-262 coastal regions were masked out in the processing of SWAMPS, we used the MODIS product MOD44WC6 (Carroll et al., 2009) to generate an ocean mask in the processing of GSW to avoid over-deducting. The coastline was buffered by 4 pixels (~1 km) into the water bodies. The buffered water was intersected with the ocean-labelled pixels from MOD44WA1 to separate the ocean from inland water. The resulting ocean mask was then applied to remove coastal wetlands in GSW. The 195 static long-term open water area excluding coastal regions in GSW is 4.5 Mkm 2 , compared with the river and stream surface areas of 0.8 Mkm 2 (Allen and Pavelsky, 2018).

Static wetland distributions
We used static wetland maps to fill gaps left by wetland types that are under-represented or missed by the SWAMPS dataset.
However, most static maps do not have global coverage or tend to have lower accuracy compared to the regional products, 200 leaving us to take a separate merging approach for each of three latitudinal bands.
Many arctic wetlands, including peatlands do not have surface inundation and thus are not captured by SWAMPS 3.2, but still emit methane. We use the Northern Circumpolar Soil Carbon Dataset (NCSCD) to map permafrost and non-permafrost peatlands based on the Histels and Histosols soil orders (Hugelius et al., 2013). The NCSCD dataset is a digital polygonbased database compiled from harmonized regional soil classification maps in which data on soil order coverage have been 205 linked to pedon data. In this study, the NCSCD wetland distribution is used as supplementary data for the latitudinal bands from 60N-90N. In this study we use a gridded version with a spatial resolution at 0.25 degrees. Permafrost and nonpermafrost peatlands (Histels and Histosols, defined as >40 cm surface peat) are mapped in the NCSCD from harmonized regional and national soil maps (Hugelius et al., 2013). However, these maps do not include occurrences of mineral soil tundra wetlands (with organic soil horizons of 0 to 40 cm) and the maps do not include smaller wetland complexes (Hugelius 210 et al., 2020). To better include these types of wetlands, the NCSCD soil maps were combined with CircumArctic Wetlands based on Advanced Aperture Radar (CAWASAR) by Widhalm et al., (2015). The SAR data identifies both organic and mineral wetland soils. It is based on ENVISAT Advanced SAR data acquired in Global Monitoring mode (medium resolution) under frozen soil conditions, what represents surface roughness which can serve as proxy for wetness levels in tundra. The wettest class was included as wetland. It corresponds to soils with >25 kg C m² in the top 100 cm (Bartsch et al., 215 2016). To avoid double counting of organic wetlands (peatlands) the datasets were overlayed so that any overlap between the datasets was removed, maintaining the NCSCD in the output data. The merged static map covers 2.3 Mkm 2 for the high latitudes (>60N), including peatlands and mineral wetlands in the tundra biomes.
The distribution of tropical wetlands, including annually or seasonally water-logged area and tropical peatlands, are derived from an expert-system mapping product (Gumbricht et al., 2017). We used the CIFOR wetland distribution for adjusting precipitation climatology from WorldClim global data set (Hijmans et al., 2005). A simplified hydrological model was used to estimate the local vertical water balance, runoff, and estimate flood volumes. The topographic and hydrologic data are 225 merged with MODIS (MCD43A4) images used for estimating the duration of wet and inundated soil conditions. The estimated area of tropical peatlands and wetlands are ~1.7 Mkm 2 and ~4.7 Mkm 2 respectively. The estimated extent of CIFOR for the Cuvette Centrale tropical African peatland in the Congo basin is 125,400 km 2 , which is in agreement with 145,500 km 2 of a recent independent field investigation (Dargie et al., 2017).
The Global Lakes and Wetland Dataset (GLWD) (Lehner and Döll, 2004) is a global database of lakes, reservoirs, and 230 wetlands based on the aggregation of aerial surveys, surveyor maps and inventories at global and regional scales. While GLWD was generated from data sources now decades old, for some regions, it still represents the most complete wetland database available today. In this study, the GLWD wetland distribution is used to cover the temperate wetland only in the latitudinal band 40N-60N, outside the range of NCSCD and CIFOR. We used the Level 3 product, a global raster map that contains 12 classes of waterbodies and wetlands at the 30-second resolution. We excluded the classes representing lakes, 235 rivers and reservoirs (1-3) and estimated the area of fractional wetland classes (9-12) as the midpoint from the range of each class. We then calculated the total fraction of wetland from all classes in 0.25-degree pixels. The estimated total wetland extent in GLWD is 8.7 Mkm 2 for the globe and 2.7 Mkm 2 for the 40N-60N bands.

Irrigated rice distributions
The distribution of rice paddies is derived from the global data set of monthly irrigated and rainfed crop areas for the year ca. 240 2000 (MIRCA2000) (Portmann et al., 2010). The datasets used to develop MIRCA2000 are based on compiling censusbased land use datasets downscaled to grid-cell level and thus is generally consistent with subnational statistics collected by national institutions and by the FAO (Food and Agriculture Organization of the United Nations). For this study, we extracted the annual maximum area of irrigated rice paddies from its original resolution at 5 arc-minute and remapped to 0.25-degree resolutions. We did not consider rainfed rice as we could not reliably separate lowland from upland cropping practices, with 245 only the latter seasonally contributing to surface inundation. The estimated rice paddies in MIRCA2000 (irrigated: 0.64 Mkm 2 ; rainfed: 1.13 Mkm 2 ) is largely consistent with census-based national and sub-national statistics from FAO (1.54 Mkm 2 for total area at ca. 2000) and slightly lower than a remote sensing estimate for irrigated (0.66 Mkm 2 ) (Salmon et al., 2015),. We thus apply the monthly rice cover from 2000 across the entire 2000-2018 time-series. This assumption ignoring year-on-year change in rice paddy area is reasonable given that its area increased by <

WAD2M evaluation
The WAD2M was evaluated against several, both static and dynamic, independent datasets of wetland area and surface inundation (Table A1). We used a set of satellite-based terrestrial water dynamics to evaluate the trends in temporal pattern 255 of WAD2M, including (1) (Zhang et al., 2018). We also compare to a global static map from Tootchi et al., 2019 (regularly flooded wetlands plus groundwater-driven wetlands based on topographic index; hereafter denoted as Tootchi2019) and region static maps available over the West Siberian Lowlands (Terentieva et al., 2016) and Amazon Basin (Hess et al., 2015).
The similarity of WAD2M performance to these the independent validation data is evaluated using the Kappa index.

Effect of data processing on the results
Globally, WAD2M (MAmax) identifies 3.6 Mkm 2 more wetlands compared to SWAMPS v3.2 (Table 1). On a continental scale, the wetland extent of SWAMPS v3.2 is in general agreement with inventories except for pronounced discrepancies for Tropical wetlands (e.g. Amazon Lowland and tropical Africa), central Asia, and the Sahel regions. The lower area of tropical wetland in the SWAMPS v3.2 is generally due to the influence of dense forest canopies. It should be noted that the 270 SWAMPS v3.2 detected higher wetland area in India than southeastern China, due to the inclusion of rice paddies in SWAMPS v3.2 that are masked out in WAD2M.  Table 1 quantifies the effect of the data processing steps on the continental and global estimates of wetland area. The total area including all water bodies such as rice paddies, rivers, streams, lakes, ponds, and reservoirs after fwmax correction are 17.0 Mkm 2 for MALt. This number is close to the downscaled GIEMS-D15 (17.3 Mkm 2 ), also produced through data merger, suggesting a good agreement between the two products. Applying the fwmax correction leads to a ca. 20% increase for the three states of inundation relative to the SWAMPS v3.2. As intended, the augmentation with inventories filled many missing 280 or underestimated wetland areas of the SWAMPS dataset, which include the Congo floodplain, Amazon Basin lowlands, the https://doi.org/10.5194/essd-2020-262 Our estimated global total wetland area is slightly higher than GIEMS2 (Table 2) but is lower than a high-resolution version of GIEMS initial version GIEMS-D15, which reports a long-term maximum of 17.3 Mkm 2 (Fluet-Chouinard et al., 2015).
Considering that WAD2M conservatively excludes rice paddies (0.59 Mkm 2 ), rivers, streams, and lakes and ponds (2.52 Mkm 2 ) while GIEMS-D15 include these water bodies, one possible conclusion is that WAD2M applies the upward mergers 295 of CIFOR and NCSCD, which has lower wetland estimates than GLWD, causing a lower long-term maximum than GIEMS-D15. In addition, our estimated total area for intermittently inundated wetlands is close to the 5.2 Mkm 2 reported for similar wetlands by GIEMS-D15, suggesting a good agreement for temporary inundated areas between two independently developed products. Other recent studies (Hu et al., 2017;Tootchi et al., 2019), however, proposed a much higher global wetland area of 27-29 Mkm 2 , which are likely overestimations due to their approaches based on topographic wetness indexes 300 that do not take into account the location of surface-water tables. This leads to an overestimation of the inundated area with shallow groundwater tables, and large inundated areas in e.g. Central Asia and South America that are not matched by other wetland maps. The latitudinal distribution of wetland area (Fig. 4) suggest that the northern hemisphere mid-to-high latitudes (> 45°N) have the highest coverage of wetland area with 45±5% of the total area of wetlands, followed by the equatorial region (10°S-315 10°N). A large portion of the intermittent wetlands are found in the northern mid-high latitudes, in regions that also have large areas of seasonal wetlands. The overall latitudinal pattern in WAD2M is similar to that of other estimates except for the Tootchi2019, which has the highest wetland area along the latitude gradient. The exception is over the mid-latitudes (20°N-40°N) where the wetland area in GLWD are more extensive than that in WAD2M. The wetland areas in the arctic (>60°N) in WAD2M have lower wetland extent than GLWD and NCSCD but higher than GIEMS2. The WAD2M shows a slightly 320 higher wetland extent in the latitudinal band of 10°N-15°N compared to the other products, which we attribute to the higher intermittent wetlands in Southeastern Asia detected by SWAMPS (Fig. 3d).The latitudinal gradient of the wetland area in WAD2M is similar to the previous version SWAMPS-GLWD (Poulter et al., 2017), but with a reduced wetland area in the Arctic (> 50°N) and at mid-latitudes (15°N-45°N), a consequence of the masking out the inland-water areas from GSW.
Surface inundation products (GIEMS2 and SWAMPS) have limited observations in the high latitudes due to underestimates 325 of wetland extent for unsaturated peatlands (Bohn et al., 2015), the presence of snow and ice, and are not reliable points of comparison in high latitudes.

Regional comparison
We validated WAD2M against available independent fine-resolution datasets for the two methane emitting hotspots, Amazon Basin Lowlands (defined as the portion of the Amazon watershed below 500 m asl.) and West Siberian Lowlands.

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These two regions represent different wetland subtypes, vegetation compositions and local hydrology, making them complimentary for our validation.
The distribution of wetland area from WAD2M shows a similar spatial pattern for the Amazon Basin Lowlands compared to the map based on JERS-1 SAR (Hess et al. 2015), which was used by Pangala et al. (2017) to estimate methane emissions.
WAD2M have a good similarity (kappa=0.54) with the independent, L-band synthetic aperture radar (SAR) map, slightly 335 lower than GIEMS2 (kappa=0.56; Fig. 6a) but higher than all other global products compared (range: 0.1-0.2). WAD2M adequately captures the permanently inundated wetlands along with the Amazon Basin river channel network as well as temporarily flooded wetlands during the wet season (Fig. 5a). However, considerable spatial disagreements of the wetland location and extent were found among available datasets when compared with Hess et al., 2015 CIFOR estimate is likely an underestimation given the limitations of its topographical hydrology approach at estimating inundation over flat terrain like the Pantanal.
The comparison of multiple wetland mapping products for the West Siberian Lowland (Fig. 5b)

Seasonal cycle
Distinctive seasonal cycles in WAD2M can be observed across varying latitudinal bands. (Fig. 7). The Tropics (30°S-30°N) contributes 68% of the global annual variation in wetland area, owing the large wetting and drying cycles of tropical 360 wetlands. Despite its large area of intermittent wetlands, the mid-latitudes have a less pronounced seasonal cycle with an average annual minimum of 0.9 Mkm 2 and average annual maximum of 1.1 Mkm 2 compared to the tropics and highlatitudes. High latitude wetlands again have a strong seasonal cycle with an average annual minimum of 0.24 Mkm 2 and average annual maximum of 1.5 Mkm 2 . The seasonal cycle of WAD2M in mid-latitude is small compared to GIEMS2 (Prigent et al., 2020), which is possibly due to different algorithms applied in SWAMPS and GIEMS2, especially in the way 365 the vegetation contribution is accounted for. The seasonal cycle in the high latitudes is highest among the three regions, which is consistent with GIEMS2 and are mainly due to significant annual freeze/thaw cycle.
Given that there is a surprising scarcity of independent wetland products to evaluate the seasonal patterns in mid-and highlatitudes, we only focus on the comparison of seasonal cycle for the Amazon Basin, the largest regional contributors to the seasonal cycle of wetland extent. For the Amazon Basin Lowlands, the estimates of wetland area exhibit a significant 370 seasonal pattern in both the WAD2M and SAR-based high-resolution estimates from Hess et al. (2015). As illustrated in

Interannual variation
The interannual variations in WAD2M suggests the effect of climate variations on global wetland extent across varying 380 latitudinal bands (Fig. 7). Monthly anomalies, calculated by subtracting the 19-year mean monthly value from the monthly time series, reveal the changes in global wetlands in response to global climate variability such as the El Niño-Southern Oscillation (ENSO) (Fig 7b). For instance, a strong positive response in wetland areal anomalies was captured by WAD2M during the strong 2010-2011 La Niña event that temporarily increase the terrestrial water storage via affecting precipitation patterns globally (Boening et al., 2012). The signal for the recovery captured by WAD2M, i.e., the decline during the late 385 stage of La Niña, is consistent with the estimated terrestrial water storage from GRACE and the ESA CCI soil moisture product (Fig. 9). The linear fit of the pan-tropical wetland anomalies for WAD2M over 2000-2018 shows no significant change (p > 0.1) in the wetland extent for the entire period, consistent with (Parker et al., 2018) that showed no trend in tropical wetland emissions using satellite based inversion of CH4 concentrations. Although the tropical regions have a net reduction of 1.3 10 3 km/yr (p < 0.05) over the 2000-2018 period. There are no trends of wetland extent for mid-latitudes and 390 high latitudes (p> 0.1) as was also found with Landsat imagery (Wulder et al., 2018).
In general, variation in surface water in the tropics is primarily driven by precipitation and the agreement in the patterns of the surface water extent and precipitation gives confidence in the inter-annual variability of wetland area estimation. At high latitude, surface-water runoff from snowmelt, not from direct precipitation, contributes towards the lower correlation

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For the interannual variations at river basin scale, there is a generally good agreement in the interannual variation of wetland extent between WAD2M and four surface water products that are based on different methodologies (Fig. 9). All the products, 2018). This can be supported by the wetland extent estimate from the TOPMODEL based prognostic hydrological approach (Zhang et al., 2018), which explicitly exclude influence of human activity and attributes the change to the enhanced tropical precipitation since 2014. Fig. 10 shows the uncertainty range (1) of mean annual maximum wetland area across the 6 global and regional data sources applied in this study. Amazon Lowland Basin and Siberian Lowland are two relatively more informed regions compared to the rest of the world (Fig. 10b). There is considerable uncertainty in wetland hotspots such as Hudson Bay Lowlands, West Siberian Lowlands, and major tropical floodplain regions. The causes of the high uncertainty for the boreal and tropical wetlands differ. Mapping boreal wetlands requires discriminating between wetlands and small ponds, which are 420 both considered as wetlands in some inventories (e.g., GLWD) but as inland waters in others (e.g., GSW). Thus, the removal of freshwater area is one reason that the boreal wetlands in WAD2M are lower. The uncertainty over tropical floodplain systems is due to the temporal mismatches of the different data sources, and the large seasonal and interannual variability in inundated area. Further, densely vegetated forest canopies in tropical floodplains can lead to systematic under-estimation of inundation from satellite-based products. Also, uncertainty in DEMs (from spatial resolution, or whether the measurements 425 are 'surface' or 'soil'), which serves as the basis of topographic index that is applied in the hybrid wetland mapping products (e.g. CIFOR, Tootchi et al., 2019), can lead to considerable uncertainty in estimation of wetland extent (Zhang et al., 2016), especially for the vegetated wetlands in complex terrain surface (Su et al., 2015).

Discussion
Due to the scarcity of ground-truth maps for representative regions, further work is needed to confirm the distribution of 430 inundation captured by WAD2M representing an improvement over existing maps. In particular, the sensitivity to subcanopy inundation, the priori knowledge of land cover applied in the retrieval algorithm, and the length of observations can affect the overall accuracy of SWAMPS and thus contribute to the uncertainty of WAD2M. For instance, WAD2M reports a vast inundated area in the Sahel region where validation of the SWAMPS retrieval algorithm is lacking due to sparsity of dynamic ground observations (Jensen and McDonald, 2019). Moreover, the decadal trends of WAD2M are influenced by the 435 inter-calibration of brightness temperature across different microwave sensors, which could potentially introduce inconsistency between different time period covered by the measurements. Thus, it is important to be cautious with the interpretation of the long-term trends based on WAD2M. Lastly, because the GSW and MIRCA2000 data sources are aggregated to 0.25 spatial resolution in the processing of WAD2M, it ignores the potential overlapping between these two mergers at fine spatial resolution, leading to unintentional double-accounting when deducting open water and rice paddies 440 from WAD2M. Future refinements to WAD2M could come from 1) improvements to revisit, spatial resolution, spectral range and signal-tonoise of remotely sensed data input and 2) refinements to our fusion methodology to use uncertainties to generate ensemble maps. Several new or upcoming satellite missions may provide improved global wetland dynamics in the future version of WAD2M. The Cyclone Global Navigation Satellite System reflectometry (CYGNSS/GNSS-R) (Nghiem et al., 2017) 445 demonstrate its capabilities to detect the inundation under different vegetation condition, which is complementary to inventories for evaluation. The NASA Surface Water and Ocean Topography (SWOT), the Copernicus L-band SAR mission Radar Observing System for Europe (ROSE-L) (Pierdicca et al., 2019), and NASA-ISRO SAR (NISAR) mission, will greatly increase our capacity to monitor the spatiotemporal dynamics of wetlands and floodplains at high spatial resolution (<50m), make it an immensely valuable resource in the future work of wetland dynamic mapping such as WAD2M.

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Commercial satellites are providing even higher-spatial resolution at daily revisit, i.e., PLANET Dove constellation, which is intercalibrated could go beyond providing static maps and provide time series of wetland data (Cooley et al., 2017).
For the methodology, combining products from different satellite sensors (e.g. optical and microwave) and inventories has been proved to be a feasible way to reduce the bias in the spatial distribution of wetlands and provide reliable estimates for the use of global wetland CH4 studies. However, the spatial resolution of WAD2M is dictated by the resolution of its input 455 data on wetland dynamic dataset unless a downscaling methodology is applied. Downscaling can also be used to improve spatial resolution using artificial neural networks (see https://hess.copernicus.org/articles/22/5341/2018/hess-22-5341-2018discussion.html) Machine learning approaches (Alemohammad et al., 2018;Kratzert et al., 2018;Wu et al., 2017) or physically-based hydrological models (Gumbricht, 2018), together with higher resolution images (e.g. Landsat, ALOS 1&2) are better suited to capture inundation features at fine scales. On the other hand, inventories at the regional and national 460 scales are needed for some less-informed wetlands (e.g. Africa, and Southeast Asia), which will help reliable validation and evaluation for these regions in the future quantitative studies of wetland. Moreover, even with better sensors in the future, improvements on wetland maps from past & future satellite will be necessary for backward extension of time series.

Conclusion
The development of a global wetland product WAD2M has demonstrated the capability to produce maps of wetlands and inundation that are consistent with independent datasets. Combining temporal dynamics from coarse resolution product date and will be useful to estimate wetland CH4 flux. WAD2M provides valuable information for a range of applications, ranging from understanding the role of floodplains to carbon modelling and general assessment of global response to climate change.

Author Contributions 475
BP and ZZ conceived the work. All authors contributed to development of the wetland dataset, and analysis of results and writing of manuscript.

State
#includes both inundated and non-inundated wetlands but excludes artificial inundation and lakes, ponds, and reservoirs.