Improving Representation of Tropical Wetland Methane Emissions With CYGNSS Inundation Maps

Wetlands are the single largest source of methane to the atmosphere and their emissions are expected to respond to a changing climate. Inaccuracy and uncertainty in inundation extent drives differences in modeled wetland emissions and impacts representation of wetland emissions on inter- annual and seasonal time frames. Existing wetland maps are based on optical or NIR satellite data obscured by clouds and vegetation, often leading to underestimates in wetlands extent, especially in the Tropics. Here, we present new inundation maps based on the Cyclone Global Navigation Satellite System (CYGNSS) satellite constellation, operating in L- band that is not impacted by clouds or vegetation, providing reliable observations through canopy and cloudy periods. We map the temporal and spatial dynamics of the Pantanal and Sudd wetlands, two of the largest wetlands in the world, using CYGNSS data and a computer vision algorithm. We link these inundation maps to methane fluxes via WetCHARTs, a global wetland methane emissions model ensemble. We contrast CYGNSS- modeled methane emissions with WetCHARTs standard runs that use monthly rainfall data from ECMWF re- analysis (ERA5), as well as the commonly used SWAMPS wetland maps. We find that the CYGNSS- based inundation maps modify the methane emissions in multiple ways. The seasonality of inundation and methane emissions is shifted by two months because of the lag in wetland recharge following peak rainfall. Both inundation and methane emissions also respond non- linearly to wet- season precipitation totals, leading to large interannual variability in emissions. Finally, the annual magnitude of emissions is found to be greater than previously estimated.Key PointsCyclone Global Navigation Satellite System (CYGNSS) data is used to produce monthly maps of tropical wetlands. The maps are used to drive the WetCHARTs methane emission modelThe seasonality of inundation- based model results lags two months behind the rainfall- based models and shows larger dry- season emissionCYGNSS- based estimates, consistent with independent observations, show higher emissions with larger variability than rainfall- driven onesPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/171217/1/gbc21210.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/171217/2/2020GB006890-sup-0001-Supporting_Information_SI-S01.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/171217/3/gbc21210_am.pd