Many maps of open water and wetland have been developed based on three main methods: (i) compiling national/regional wetland surveys; (ii) identifying inundated areas by satellite imagery; (iii) delineating wetlands as shallow water table areas based on groundwater modelling. The resulting global wetland extents, however, vary from 3 to 21% of the land surface area, because of inconsistencies in wetland definitions and limitations in observation or modelling systems. To reconcile these differences, we propose composite wetland (CW) maps combining two classes of wetlands: (1) regularly flooded wetlands (RFW) which are obtained by overlapping selected open-water and inundation datasets; (2) groundwater-driven wetlands (GDW) derived from groundwater modelling (either direct or simplified using several variants of the topographic index). Wetlands are thus statically defined as areas with persistent near saturated soil because of regular flooding or shallow groundwater. To explore the uncertainty of the proposed data fusion, seven CW maps were generated at the 15 arc-sec resolution (ca 500 m at the Equator) using geographic information system (GIS) tools, by combining one RFW and different GDW maps. They correspond to contemporary potential wetlands, i.e. the expected wetlands assuming no human influence under the present climate. To validate the approach, these CW maps were compared to existing wetland datasets at the global and regional scales: the spatial patterns are decently captured, but the wetland extents are difficult to assess against the dispersion of the validation datasets. Compared to the only regional dataset encompassing both GDWs and RFWs, over France, the CW maps perform well and better than all other considered global wetland datasets. Two CW maps, showing the best overall match with the available evaluation datasets, are eventually selected. They give a global wetland extent of 27.5 and 29 million km², i.e. 21.1 and 21.6% of global land area, which is among the highest values in the literature, in line with recent estimates also recognizing the contribution of GDWs. This wetland class covers 15% of global land area, against 9.7% for RFWs (with an overlap ca 3.4 %), including wetlands under canopy/cloud cover leading to high wetland densities in the tropics, and small scattered wetlands, which cover less than 5% of land but are very important for hydrological and ecological functioning in temperate to arid areas. […]

DescriptionThis dataset shows a distribution of wetland that covers the tropics and sub tropics (38° N to 56° S; 161° E to 117° W), excluding small islands. It was mapped in 236 meters spatial resolution by combining a hydrological model and annual time series of satellite-derived estimates of soil moisture to represent water flow and surface wetness that are then combined with geomorphological data. The dataset consist of 6 classes that covers: Fen, Bog-ombrotrophic peat domes, Riverine, Mangrove, Flood-out, Floodplain, Swamp and Marsh.

This data set estimates large-scale wetland distributions and important wetland complexes, including areas of marsh, fen, peatland, and water (Lehner and Döll 2004). Large rivers are also included as wetlands (lotic wetlands); it is assumed that only a river with adjacent wetlands (floodplain) is wide enough to appear as a polygon on the coarse-scale source maps. Wetlands are a crucial part of natural infrastructure as they help protect water quality, hold excess flood water, stabilize shoreline, and help recharge groundwater (Beeson and Doyle 1995, Stuart and Edwards 2006). Limited by sources, the data set refers to lakes as permanent still-water bodies (lentic water bodies) without direct connection to the sea, including saline lakes and lagoons as lakes, while excluding intermittent or ephemeral water bodies. Lakes that are manmade are explicitly classified as reservoirs. The Global Lakes and Wetlands Database combines best available sources for lakes and wetlands on a global scale. This data set includes information on large lakes (area ≥ 50 km2) and reservoirs (storage capacity ≥ 0.5 km3), permanent open water bodies (surface area ≥ 0.1 km2), and maximum extent and types of wetlands.

This repository contains three datasets resulting from the reconstruction of global wetland loss over 1700-2020. The three datasets are listed here and described in more detail below:

A. National and subnational statistics of drained or converted areas

B. Regional wetland percentage loss estimates and geospatial polygons

C. Gridded reconstruction

The scripts used to process input data, model and calibrate the wetland loss reconstruction, and produce the figures are publicly available at https://github.com/etiennefluetchouinard/wetland-loss-reconstruction.

A. National and subnational statistics of drained or converted areas

This tabular database containing national and subnational statistics of wetland area drained and peat mass extracted. The database includes four land use types: cropland, forestry, peat extraction and wetland cultivation. These data are used as input to the mapped wetland loss reconstruction. Column descriptions of drainage_db_v10.csv:

unit: Scale of administrative unit ("national" or "subnational").

type: Land use type ("Cropland", "Forestry", "Peat Extraction" or "Wetland Cultivation")

iso_a3: 3-letter code of each country.

region: Name of subnational unit. Blank if data is national scale.

HASC_1: Hierarchical Administrative Subdivisions Codes for the subnational units. Blank if data is national scale.

year: Year of data.

drained_area_1000ha: Cumulative area drained by the year specified, in thousands of hectares.

drained_weight_1000tonsyr: Annual peat extraction rate for the year, in thousand tons per year.

peatland_only: Label indicating whether the drained area applies to all wetlands or peatlands specifically ("Peatland only" or blank).

Comment: Additional description from original data source, or unit conversion, or data corrections.

Source: Reference of data source and/or compilers.

B. Regional wetland percentage loss estimates and geospatial polygons

A shapefile of 151 polygons projected in WGS84. Columns description for polygon shapefile of the regional wetland loss percentage: regional_loss_poly.shp:

id: Numerical identifier.

name: Name of administrative unit, region or water feature the polygon area covers.

country: Name of country.

continent: Name of continent.

wet_categ: Broad category of wetlands included in the estimate (“Peatlands”, “Inland natural wetlands”, “Coastal natural wetlands”, “Unspecified natural type(s)” or “All wetlands”).

yr_start: Start year of the period over which wetland loss is estimated.

yr_end: End year of the period over which wetland loss is estimated.

area_mkm2: Surface area of the polygon, in million square kilometers (Mkm2).

perc_loss: Numerical value of percentage wetland loss (positive value represent loss of wetland area between start and end year.

comment: Additional description of estimate used or estimation method.

source: Citation of original data source.

compiler: Citation of intermediary data compiler.

C. Gridded reconstruction

Gridded outputs are stored in a separate NetCDF file for each of the 12 reconstructions of simulated wetland and present-day wetland maps. An ensemble average was also computed from the 12 reconstructions (only individual reconstructions were discussed in the manuscript). These data consist of global maps generated from the drainage reconstruction methodology for 33 decadal intervals (1700-2020 inclusive) for 9 variables:

The filenames of ensemble members are labelled to with the name of the input present-day and simulated wetland maps:

    “wetland_loss_1700-2020_” + simulated input + “_” + present-day input + “_v10.nc”

The 4 simulated wetland map inputs are: LPJwsl, SDGVM, ORCHIDEE, DLEM. The 3 present-day wetland map inputs are: GIEMSv2, GLWD3, WAD2M.

Description of the 9 variables in each NCDF file:

wetland_loss: Cumulative wetland area lost (km2 per grid cell). This variable is equivalent to the sum of area drained for the seven land uses drained

nat_wetland: Remaining natural wetland area (km2 per grid cell)

cropland: Cropland area drained (km2 per grid cell) leading to wetland loss

forestry: Forestry area drained (km2 per grid cell) leading to wetland loss

peatextr: Peat harvest area drained (km2 per grid cell) leading to wetland loss

wetcultiv: Wetland cultivation area (km2 per grid cell) leading to wetland loss

ir_rice: Irrigated rice area leading to wetland loss (km2 per grid cell)

pasture: Pasture area drained leading to wetland loss (km2 per grid cell)

urban: Urban area drained leading to wetland loss (km2 per grid cell)

All layers were capped below the land pixel area grid (from HYDE 3.2, excl. open water).

Time: 33 slices; numerical years spread at decadal intervals, ranging between 1700-2020 (inclusive)

Extent: Longitude: -180° to 180°. Latitude: -56° to 84°.

See the README file for a more detailed description of this dataset. Anyone wishing to use this dataset should cite Fluet-Chouinard et al. 2023. Please contact Etienne Fluet-Chouinard at etienne.fluet@gmail.com with any questions or comments with regards to the best usage of our dataset.

Fluet-Chouinard E., Stocker B., Zhang Z., Malhotra A., Melton J.R., Poulter B., Kaplan J., Goldewijk K.K., Siebert S., Minayeva T., Hugelius G., Prigent C., Aires F., Hoyt A., Davidson N., Finlayson C.M., Lehner B., Jackson R.B., McIntyre P.B. Nature. Extensive global wetland loss over the last three centuries

To solve the high-frequency sample needs of time series wetland classification, we developed a method for automatically producing global wetland samples based on 13 global and regional wetland-related datasets and millions of images from Landsat 8 OLI, MODIS, Sentinel-1 SAR GRD, and Sentinel-2 MSI sensors. Considering the consistency of types and the separability of spectra, we summarized all classification systems into three types: wetland, water body, and non-wetland.Samples are randomly selected based on the equal-area stratified sampling scheme based on the existence probability of wetlands. In order to ensure sufficient samples, we proposed global sample size of 500,000. According to the global potential wetland distribution data set, the sample size of each grid was allocated, and samples were randomly selected. Based on 13 auxiliary data sets, we first determined the sample type according to the order of water body and wetland and assigned the "non-wetland" attribute to the type of neither water body nor wetland. The 13 auxiliary data sets include GlobeLand30 (Chen et al., 2014), FROM-GLC (Yu et al., 2013), GlobCover (Arino et al., 2010), GLC_FCS30_2020 (Liu et al., 2020), Joint Research Centre Global Surface Water Survey and Mapping map (Pekel et al., 2016), Global Reservoir and Dam Database (GRanD) (Lehner et al., 2011), Global Mangrove Watch (GMW) (Bunting et al., 2018), Global Lakes and Wetlands Database (GLWD) (Lehner et al., 2004), Murray Global Intertidal Change (MGIC) (Murray et al., 2019), CAS_Wetlands (Mao et al., 2020), CA_wetlands (Wulder et al., 2018), National Land Cover Database (NLCD) (Yang et al., 2018), Global Potential Wetland Distribution Dataset (GPWD) (Hu et al., 2017).We also included 139027 Landsat 8 OLI images, 21160 MOD09A1 images, 296479 Sentinel-1 SAR images, and 4553453 Sentinel-2 MSI images globally from January 1 to December 31, 2020. We extracted minimum, maximum, mean, and median information for each band and NDVI, NDWI, MNDWI, and LSWI indexes in four sensors of global wetland samples. In order to remove this part of the noise, this study kept the water, wetland, and non-wetland samples within one standard deviation of the annual mean of each spectral band as the sample's secondary screening conditions to ensure the accuracy of samples.The number of wetland samples determined by each sensor is different. Landsat 8 has a total of 202,111 samples, including 13,176 water bodies, 54,229 wetland samples, and 134,706 non-wetland samples; MODIS has a total of 190,898 samples, including 13,436 water body samples, 50,400 wetland samples, and 127,062 non-wetland samples ; Sentinel- has a total of 185,943 samples, including 10,885 water samples, 54,224 wetland samples, and 120,834 non-wetland samples; Sentinel-2 has a total of 185,484 samples, including 11,225 water samples, 52,142 wetland samples, and 122,117 non-wetland samples.They are stored separately in four shapefiles.

This dataset provides monthly globally gridded freshwater wetland methane emissions from 2001-2018 in nmol CH4 m-2 s-1, g C-CH4 m-2 d-1, and TgCH4 grid cell-1 month-1. The data were derived from a six-predictor random forest upscaling model (UpCH4) trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites covering bog (8), fen (8), marsh (10), swamp (6), and wet tundra (11) wetland classes and distributed across Arctic-boreal (20), temperate (16), and (sub)tropical (7) climate zones. Weekly mean CH4 fluxes were computed from half-hourly FLUXNET-CH4 Version 1.0 fluxes. Each grid cell CH4 flux prediction was weighted by fractional grid cell wetland extent to estimate CH4 emissions using the primary global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) product and an alternate Global Inundation Estimate from Multiple Satellites GIEMS version 2 global wetland map. Both WAD2M and GIEMS-2 maps were modified with several correction data layers to represent the monthly area covered by vegetated wetlands, excluding open water and coastal wetlands. The data products are: mean daily fluxes with no adjustment for wetland area (i.e., flux densities assuming hypothetical 100% wetland cover); mean daily fluxes adjusting for WAD2M or GIEMS-2 wetland area; and by-pixel monthly sum of freshwater wetland methane emissions adjusting for WAD2M or GIEMS-2 wetland area. The data are provided in NetCDF4 format.

Dynamics of global wetlands are closely linked to biodiversity conservation, hydrology, and greenhouse gas emissions. However, long-term time series of global wetland products are still lacking. Using a TOPMODEL-based diagnostic model, we produced an ensemble of 28 gridded maps of monthly global/regional wetland extent products at 0.25° × 0.25° spatial resolution based on four observation-based wetland products and seven reanalysis soil moisture (SM) data sets for the period going from 1980 to 2017. The parameters of the model are calibrated on grid-scale against four observation-based wetland products including the dynamic Global Inundation Estimation from Multiple Satellites version 2 (GIEMS-2; Prigent et al., 2019), the static Regularly Flooded Wetland map (RFW; Tootchi et al., 2019), the dynamic Surface Water Microwave Product Series (SWAMPS; Schroeder et al., 2015; Poulter et al., 2017), and the static wetland area from Gumbricht et al. (2017) limited to the tropics (G2017). Overall, our products can capture the spatial distributions, seasonal cycles, and interannual variabilities of observed wetland extent well, and also show a good agreement with independent terrestrial water storage (TWS) estimates derived from the Gravity Recovery and Climate Experiment (GRACE) satellites (Tapley et al., 2004; Wouters et al., 2014). The dispersion of the 28 spatio-temporal datasets shows the large uncertainties in the estimation of global absolute wetland extent, which result from uncertainties in SM data, the choice of observed wetland data sets and calibration methods, as well as limitations of TOPMODEL. The simulated mean annual maximum global wetland area calibrated by GIEMS-2, RFW, and SWAMPS fluctuates within a range of 3.8–4.8 Mkm2, 3.8–4.9 Mkm2, and 4.8–5.7 Mkm2 respectively. The long temporal coverage beyond the era of satellite datasets, the global coverage and the opportunity to provide real time update from ongoing SM data make these products helpful for various applications such as analyzing the wetland-related methane emission.

Several layers describing density of surface water / streams projected to the Good Homolosine projection. List of layers included:

• hyd_log1p.upstream.area_merit.hydro_m = Upstream Drainage Area based on the MERIT Hydro,
• hyd_river.density_gloric_p = rasterized Global River Classification (GLORIC) DB,
• lcv_water.occurance_jrc.surfacewater_p = Surface Water based on the JRC's Global Surface Water,
• lcv_water.seasonal_probav.glc.lc100_p = Seasonal Inland Water probability based on the Copernicus LC100 map,
• lcv_wetlands.cw_upmc.wtd_c = composite wetland (CW) map based on Tootchi et al. (2019),
• Goode_Homolosine_domain_250m.tif = map domain prepared by Luís de Sousa,
• tiles_GH_100km_land.gpkg = 100 km x 100 km tiling system covering the land mass,

Important notes: Processing steps are described in detail here. Antartica is not included. Reprojecting maps to Goode Homolosine projection can be cumbersome and small amount of artifacts at the edges of the map can be anticipated.

These maps were develop in connection to the OpenLandMap.org initiative.

If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:

• Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues
• General questions and comments: https://disqus.com/home/forums/landgis/

All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:

• hyd = theme: hydrology and water dynamics,
• log1p.upstream.area = variable: log(X+1)*10 of the upstream area,
• merit.hydro = determination method: MERIT Hydro,
• m = mean value,
• 250m = spatial resolution / block support: 250 m,
• b0..0cm = vertical reference: surface,
• 2017 = time reference: period 2017,
• v0.1 = version number: 0.1,

Wetland methane emissions from Zhang et al. (2017) and global sulfate deposition map from Rubin et al (2023) were shared with permission from the authors. Historical climate data from WorldClim 2.1 is available from: https://www.worldclim.org/data/worldclim21.html (Fick and Hijmans 2017). Future climate data is available from the World Climate Research Program through its Working Group on Coupled Modelling at https://www.worldclim.org/data/cmip6/cmip6climate.html (Eyring et al. 2016). Klöppen-Geiger climate zone maps is available on Figshare at https://doi.org/10.6084/m9.figshare.c.6395666.v1 (Beck et al. 2023a-b) The global wetland map is available from https://zenodo.org/records/7293597 (Fluet-Chouinard et al. 2022, 2023).CitationsBeck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N. J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A. I. J. M., & Miralles, D. G. (2023a). High-resolution (1 km) Köppen-Geiger maps for 1901-2099 based on constrained CMIP6 projections. Scientific Data, 10(1), 724. https://doi.org/10.1038/s41597-023-02549-6Beck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N. J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A. I. J. M., & Miralles, D. G. (2023b) High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Figshare. Collection https://doi.org/10.6084/m9.figshare.c.6395666.v1Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, R.J., & Taylor, K.E. (2016) Geoscientific Model Development, 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016Fick, S.E. & Hijmans, R.J. (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315. https://doi.org/10.1002/joc.5086Fluet-Chouinard, E., Stocker, B.D., Zhang, Z., Malhotra, A., Melton, J.R., Poulter, B., … McIntyre, P.B. G. (2022). Global wetland loss reconstruction over 1700-2020. Zenodo. https://doi.org/10.5281/zenodo.7293597Fluet-Chouinard, E., Stocker, B.D., Zhang, Z., Malhotra, A., Melton, J.R., Poulter, B., Kaplan, J.O., Goldewijk, K.K., Siebert, S., Minayeva, T. & Hugelius, G. (2023). Extensive global wetland loss over the past three centuries. Nature, 614(7947), 281-286. https://doi.org/10.1038/s41586-022-05572-6Rubin, H.J., Fu J.S., Dentener, F., Li, R., Huang, K. & Fu, H. (2023). Global nitrogen and sulfur deposition mapping using a measurement–model fusion approach. Atmospheric Chemistry and Physics, 23(12), 7091–7102. https://doi.org/10.5194/acp-23-7091-2023Zhang, Z., Zimmermann, N.E., Stenke, A., Li, X., Hodson, E.L., Zhu, G., … Poulter, B. (2017). Emerging role of wetland methane emissions in driving 21st century climate change. Proceedings of the National Academy of Sciences of the United States of America, 114(36), 9647–9652. https://doi.org/10.1073/pnas.1618765114

A circumpolar representative and consistent wetland map is required for a range of applications ranging from upscaling of carbon fluxes and pools to climate modelling and wildlife habitat assessment. Currently available data sets lack sufficient accuracy and/or thematic detail in many regions of the Arctic. Synthetic aperture radar (SAR) data from satellites have already been shown to be suitable for wetland mapping. Envisat Advanced SAR (ASAR) provides global medium-resolution data which are examined with particular focus on spatial wetness patterns in this study. It was found that winter minimum backscatter values as well as their differences to summer minimum values reflect vegetation physiognomy units of certain wetness regimes. Low winter backscatter values are mostly found in areas vegetated by plant communities typically for wet regions in the tundra biome, due to low roughness and low volume scattering caused by the predominant vegetation. Summer to winter difference backscatter values, which in contrast to the winter values depend almost solely on soil moisture content, show expected higher values for wet regions. While the approach using difference values would seem more reasonable in order to delineate wetness patterns considering its direct link to soil moisture, it was found that a classification of winter minimum backscatter values is more applicable in tundra regions due to its better separability into wetness classes. Previous approaches for wetland detection have investigated the impact of liquid water in the soil on backscatter conditions. In this study the absence of liquid water is utilized. […]

Data input files for the paper by EA Ury et al. Managing the global wetland methane-climate feedback: A review of potential options (2024) Global Change BiologyWetland methane emissions from Zhang et al. (2017) and global sulfate deposition map from Rubin et al (2023) were shared with permission from the authors. Historical climate data from WorldClim 2.1 is available from: https://www.worldclim.org/data/worldclim21.html (Fick and Hijmans 2017). Future climate data is available from the World Climate Research Program through its Working Group on Coupled Modelling at https://www.worldclim.org/data/cmip6/cmip6climate.html (Eyring et al. 2016). Klöppen-Geiger climate zone maps is available on Figshare at https://doi.org/10.6084/m9.figshare.c.6395666.v1 (Beck et al. 2023a-b) The global wetland map is available from https://zenodo.org/records/7293597 (Fluet-Chouinard et al. 2022, 2023).CitationsBeck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N. J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A. I. J. M., & Miralles, D. G. (2023a). High-resolution (1 km) Köppen-Geiger maps for 1901-2099 based on constrained CMIP6 projections. Scientific Data, 10(1), 724. https://doi.org/10.1038/s41597-023-02549-6Beck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N. J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A. I. J. M., & Miralles, D. G. (2023b) High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Figshare. Collection https://doi.org/10.6084/m9.figshare.c.6395666.v1Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, R.J., & Taylor, K.E. (2016) Geoscientific Model Development, 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016Fick, S.E. & Hijmans, R.J. (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315. https://doi.org/10.1002/joc.5086Fluet-Chouinard, E., Stocker, B.D., Zhang, Z., Malhotra, A., Melton, J.R., Poulter, B., … McIntyre, P.B. G. (2022). Global wetland loss reconstruction over 1700-2020. Zenodo. https://doi.org/10.5281/zenodo.7293597Fluet-Chouinard, E., Stocker, B.D., Zhang, Z., Malhotra, A., Melton, J.R., Poulter, B., Kaplan, J.O., Goldewijk, K.K., Siebert, S., Minayeva, T. & Hugelius, G. (2023). Extensive global wetland loss over the past three centuries. Nature, 614(7947), 281-286. https://doi.org/10.1038/s41586-022-05572-6Rubin, H.J., Fu J.S., Dentener, F., Li, R., Huang, K. & Fu, H. (2023). Global nitrogen and sulfur deposition mapping using a measurement–model fusion approach. Atmospheric Chemistry and Physics, 23(12), 7091–7102. https://doi.org/10.5194/acp-23-7091-2023Zhang, Z., Zimmermann, N.E., Stenke, A., Li, X., Hodson, E.L., Zhu, G., … Poulter, B. (2017). Emerging role of wetland methane emissions in driving 21st century climate change. Proceedings of the National Academy of Sciences of the United States of America, 114(36), 9647–9652. https://doi.org/10.1073/pnas.1618765114

Overview: The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°). The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MAMin; total area, 6.5 × 106 km2), mean annual maximum (MAMax; 12.1 × 106 km2), and long-term maximum (LTMax; 17.3 × 106 km2). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.

Projection: WGS84 (EPSG:4326)

Geographic extent:

Longitude: -180° to 180°

Latitude: -56° to 84°

Spatial resolution: 15 arc-second (500m at equator)

Legend (for discrete pixel values):

0 = Upland

1 = Mean Annual Minimum (MAMin)

2 = Mean Annual Maximum (MAMax)

3 = Long Term Maximum (LTMax)

This dataset provides mapped tidal wetland gross primary production (GPP) estimates (g C/m2/day) derived from multiple wetland types at 250-m resolution across the conterminous United States at 16-day intervals from March 5, 2000, through November 17, 2019. GPP was derived with the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field-based eddy covariance (EC) tower GPP data into a single Bayesian framework along with Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets. Tidal wetlands are a critical component of global climate regulation. Tidal wetland-based carbon, or "blue carbon," is a valued resource that is increasingly important for restoration and conservation purposes.

Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000-2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and seagrasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km2 (Mkm2), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high 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 improve estimates of wetland CH4 fluxes for global-scale land surface modeling.

The third generation of high resolution 10-m wetland inventory map of Canada, covering an approximate area of one billion hectares, was generated using multi-year (2016-2020), multi-source imagery (Sentinel-1, Sentinel-2, ALOS PALSAR-2, and SRTM) Earth Observation (EO) data as well as environmental features. Over 8800 wetland polygons were processed within an object-based random forest classification scheme on the Google Earth Engine cloud computing platform. The average overall accuracy of 90.5% is an increase of 4.7% over CWIM2. CWIM Versions: The Canadian Wetland Inventory Map (CWIM) is an extension of work started at Memorial University to produce a Newfoundland and Labrador wetland inventory during 2015-2018 which was significantly funded by Environment and Climate Change Canada. The first national CWIM was produced 2018-2019 as a collaboration between Memorial University, C-CORE, and Natural Resources Canada. Dr. Brian Brisco was instrumental in connecting ground truth from multiple sources to the project and providing guidance. Version 2 was produced in 2020 which included more training data and processing by Canada’s ecozones rather than provinces to take advantage of the commonality of landscape ecological features within ecozones to improve the accuracy. Version 3 produced in 2021 continued adding more data sources to further improve accuracy specifically an overestimation of wetland area as well as introducing a confidence map. Version 3A completed in 2022 updates only the arctic ecozones due to their relatively lower accuracy and added hydro-physiographic data layers. Currently work is underway to create a northern circumpolar wetland inventory map to be published in 2025. Paper on Newfoundland and Labrador Wetland Inventory: Mahdianpari, M.; Salehi, B.; Mohammadimanesh, F.; Homayouni, S.; Gill, E. The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Remote Sens. 2019, 11, 43. https://doi.org/10.3390/rs11010043 Paper on CWIM1: Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Brisco, B., Homayouni, S., Gill, E., … Bourgeau-Chavez, L. (2020). Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Canadian Journal of Remote Sensing, 46(1), 15–33. https://doi.org/10.1080/07038992.2019.1711366 Paper on CWIM2: Mahdianpari, M., Brisco, B., Granger, J. E., Mohammadimanesh, F., Salehi, B., Banks, S., … Weng, Q. (2020). The Second Generation Canadian Wetland Inventory Map at 10 Meters Resolution Using Google Earth Engine. Canadian Journal of Remote Sensing, 46(3), 360–375. https://doi.org/10.1080/07038992.2020.1802584 Paper on CWIM3: M. Mahdianpari et al., "The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 8789-8803, 2021, doi: 10.1109/JSTARS.2021.3105645. Paper on Arctic ecoregion enhancement for CWIM3A: Michael Merchant, et al., ”Leveraging google earth engine cloud computing for large-scale arctic wetland mapping,” in International Journal of Applied Earth Observation and Geoinformation, vol. 125, 2023, https://doi.org/10.1016/j.jag.2023.103589.

This repository contains the data and code used to create the high mountain wetland maps presented in the article 'A map of high mountain wetlands in the world's major mountain regions'. A link to the article will be available, once it is published.

A Wetland of International Importance under the Ramsar Convention (RAMSAR) is part of the Protected Natural Areas (ENP) that are designated or managed within an international, Community, national or local framework to achieve specific objectives for the conservation of the natural heritage.A Wetland of International Importance of the Ramsar Convention is a designated area under the Convention on Wetlands of International Importance, particularly as Waterbird Habitats, whose treaty was signed in 1971 on the Caspian Sea (Iran). Its entry into force dates from 1975, the ratification by France of 1986.The inclusion on the World List of Ramsar Areas presupposes that the area meets one or more criteria demonstrating its international importance.Reference Texts: Ramsar Convention (Iran) of 2 February 1971 and Act of Ratification of the Convention. The objectives are to halt the trend of wetlands becoming extirpated, to promote the conservation of wetlands, their flora and fauna, and to promote and promote the wise use of wetlands. The addition of sites to the list is done by the State which submits duly substantiated proposals to the Ramsar Convention Bureau. In practice, the DREALs carry out the technical files under the authority of the prefects. They are then validated by the National Ramsar Committee set up by the Minister for the Environment. (definition from: Aten, legal sheets 2005) GIS layer: N_ENP_RAMSAR_S_R44.shp

Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH₄) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH₄ modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000-2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and seagrasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km² (Mkm²), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm², seasonally inundated wetlands at 4.0 Mkm² (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm² (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high 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 improve estimates of wetland CH₄ fluxes for global-scale land surface modeling.

Update: Oct.08.2021

Documentation for WAD2M Version 2.0 can be found at WAD2M_V2_update

PEATMAP is a GIS shapefile dataset that shows a distribution of peatlands that covers the entire world. It was produced by combining the most high quality available peatland map from a wide variety of sources that describe peatland distributions at global, regional and national levels. The following sequence of comparisons to discriminate between overlapping data sources were used: (1) Relevance. The most important criterion was that source data are able to identify peatlands faithfully and to distinguish them from other land cover types, especially non-peat forming wetlands. (2) Spatial resolution. In areas where two or more overlapping data sources were indistinguishable in terms of their relevance to peatlands, the dataset with the finest spatial resolution was selected. (3) Age. In any areas where two or more overlapping datasets were indistinguishable based on both their apparent relevance to peatlands and their spatial resolution, the data product that had been most recently updated was selected. Recently updated products commonly contain much older source data, the period over which the latest revision source data were collected as the primary measure of the age of a dataset.

Citation

In order to use these data, you must cite this data set with the following citation:

Xu, Jiren and Morris, Paul J. and Liu, Junguo and Holden, Joseph (2017) PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. University of Leeds. [Dataset] https://doi.org/10.5518/252

Incorporated in February 1990, the City of SeaTac is located in the Pacific Northwest, approximately midway between the cities of Seattle and Tacoma in the State of Washington. SeaTac is a vibrant community, economically strong, environmentally sensitive, and people-oriented. The City boundaries surround the Seattle-Tacoma International Airport, (approximately 3 square miles in area) which is owned and operated by the Port of Seattle. For additional information regarding the City of SeaTac, its people, or services, please visit https://www.seatacwa.gov. For additional information regarding City GIS data or maps, please visit https://www.seatacwa.gov/our-city/maps-and-gis.