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

Abstract. Seasonal and interannual variations in global wetland area are a strong driver of fluctuations methane (CH4) emissions. Current maps extent vary their definition, causing substantial disagreement between large uncertainty estimates To reconcile these differences for large-scale CH4 modeling, we developed the Wetland Area Dynamics Methane Modeling (WAD2M) version 1.0 dataset at ? 25 km resolution Equator (0.25?) monthly time step 2000–2018. WAD2M combines series surface inundation based on active passive microwave remote sensing coarse with six static datasets that discriminate inland waters, agriculture, shoreline, non-inundated wetlands. We excluded all permanent water bodies (e.g., lakes, ponds, rivers, reservoirs), coastal wetlands mangroves sea grasses), rice paddies to only represent spatiotemporal patterns inundated vegetated Globally, long-term maximum 13.0×106 km2 (13.0 Mkm2), which can be divided into three categories: mean annual minimum 3.5 Mkm2, seasonally 4.0 Mkm2 (mean minus minimum), intermittently 5.5 (long-term maximum). shows good spatial agreements independent inventories major complexes, i.e., Amazon Basin lowlands West Siberian lowlands, Cohen's kappa coefficient 0.54 0.70 respectively among multiple products. By evaluating temporal variation against modeled prognostic (i.e., TOPMODEL) satellite observations soil moisture, show it adequately represents as well effect El Niño–Southern Oscillation extent. This will improve fluxes global-scale land modeling. The found https://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).