Monthly global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) from Remote Sensing, 2000-2020

Zhang, Zhen; Fluet-Chouinard, Etienne; Jensen, Katherine; McDonald, Kyle; Hugelius, Gustaf; Gumbricht, Thomas; Carroll, Mark; Prigent, Catherine; Bartsch, Annett; Poulter, Benjamin

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.


Zhang, Zhen

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Additional Info

Field Value
Identifier DOI:10.5281/zenodo.3998453
Institute Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA
Publication Date 2021-10-08
Version 2.0
Product global WAD2M dataset
Sensor Remote Sensing
  1. Documentation for WAD2M Version 2.0 PDF
  2. WAD2M wetlands 2000-2018 0.25 arc-degree netCDF
  3. Version 2 WAD2M wetlands 2000-2020 0.25 arc-degree netCDF
  4. Version 2 WAD2M wetlands 2000-2020 0.5 arc-degree netCDF
Variables [Units]
  1. NETCDF_DIM_time: Days since 1992-01-01
Region Global
Spatial Reference EPSG:4326 WGS 84
Spatial Resolution 25 km, 50 km
Spatial Coverage Latitude -90.00 to 90.00, Longitude -180.00 to 180.00
Temporal Coverage 2000 to 2020
Temporal Resolution monthly
Format netCDF
Is Supplement To

Zhang, Z., Fluet-Chouinard, E., Jensen, K., McDonald, K., Hugelius, G., Gumbricht, T., Carroll, M., Prigent, C., Bartsch, A., and Poulter, B.: Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) , Earth Syst. Sci. Data, 13, 2001–2023,, 2021.

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Dataset extent

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