Improved prediction of tropical wetland methane emissions using new CYGNSS-based inundation maps

Abstract:

Wetlands are the largest natural source of methane and their emissions are sensitive to changes in temperature and precipitation associated with climate change. When modeling and predicting methane efflux from wetlands, inundation extent is one of the largest sources of uncertainty. Existing wetland maps are often based on optical or NIR satellite data that cannot see through clouds or vegetation, which can lead to underestimates of wetland extent and seasonal and interannual variations and maxima. These limitation present particular challenges in the Tropics, where extensive vegetation exists and cloud cover during the rainy season can last months. Here, we present new inundation extent maps based on the CYGNSS satellite constellation. Operating in L-band, CYGNSS can “see” wetlands even under cloud cover or vegetation, providing regular, reliable maps of the flooding extent.

The temporal and spatial dynamics of the Pantanal and the Sudd wetlands, two of the largest wetlands in the world, are mapped using CYGNSS data and a computer vision algorithm. The maps are then incorporated into WetCHARTs, a global wetland methane emissions model. The modeled methane emissions are compared to WetCHARTs standard runs that use static wetland maps and rainfall data from ERA5. We find that the use of the CYGNSS-based inundation maps modifies predicted methane emissions in several ways. The seasonality of emissions is shifted by a month, consistent with a lag in wetland recharge following peak rainfall. In addition, while the wet-to-dry season increase in emissions is comparable across models, there is a clear increase in both minimum and maximum emissions in the CYGNSS-based simulation. We find this residual dry season methane emission predicted by the CYGNSS-driven simulations to be more consistent with local flux tower data. Finally, we also consider the total annual emissions over the two wetlands and evaluate which simulation is most consistent with remotely sensed methane emissions measurements.

The CYGNSS-based maps we present here provide new information about tropical wetland extent that significantly modifies methane emissions predicted from WetCHARTS, providing a new observational constraint that can improve our ability to represent tropical wetland emissions and their seasonal and interannual variability.