Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)
- 1Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA
- 2Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
- 3Department of Earth and Atmospheric Sciences, City College of New York, City University of New York, New York, NY 10031, USA
- 4Department of Earth and Environmental Science, The Graduate Center, City University of New York, New York, NY 10031, USA
- 5Carbon Cycle and Ecosystems Group, Jet Propulsion Laboratory, California Institute of Technology. 4800 Oak Grove Drive, Pasadena. CA 91001, USA
- 6Department of Physical Geography, Stockholm University, 10691 Stockholm, Sweden
- 7Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden
- 8Computational and Information Science and Technology Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
- 9CNRS, Sorbonne Université, Observatoire de Paris, Université PSL, Lerma, Paris, France
- 10b.geos Industriestrasse 1, 2100 Korneuburg, Austria
- 11Austrian Polar Research Institute, UZA1, Althanstrae 14, 1090 Wien, Austria
- 12Biospheric Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Abstract. 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 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 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 dataset can be found at http://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).
Zhen Zhang et al.
Zhen Zhang et al.
Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) https://doi.org/10.5281/zenodo.3998454
Zhen Zhang et al.
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