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

TLDR: Wang et al. as mentioned in this paper developed the global Wetland Area and Dynamics for Methane Modeling (WAD2M) version 1.0 dataset, which combines a time series of surface inundation based on active and passive microwave remote sensing at a coarse-resolution with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands.

Abstract: . Seasonal and interannual variations in global wetland area are 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 between and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH 4 modeling, we developed the global Wetland Area and Dynamics for Methane Modeling (WAD2M) version 1.0 dataset at a ∼  25 km resolution at the Equator (0.25 ∘ ) at a monthly time step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at a coarse resolution with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We excluded all permanent water bodies (e.g., lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), 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 13.0×106  km 2 (13.0 Mkm 2 ), which can be divided into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm 2 , seasonally inundated wetlands at 4.0 Mkm 2 (mean annual maximum minus mean annual 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 Basin lowlands and West Siberian lowlands, with Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetland products. By evaluating the temporal variation in 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 Nino–Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH 4 fluxes for global-scale land surface modeling. The dataset can be found at https://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).