Methane Emissions from Bangladesh: Bridging the Gap Between Ground-based and Space-borne Estimates

Abstract:

Gaining an understanding of methane (CH₄) emission sources and atmospheric dispersion is an essential part of climate change research. Large-scale and global studies often rely on satellite observations of column CH₄ mixing ratio whereas high-spatial resolution estimates rely on ground-based measurements. Extrapolation of ground-based measurements on, for example, rice paddies to broad region scales is highly uncertain because of spatio-temporal variability. We explore the use of ground-based river stage measurements and independent satellite observations of flooded area along with satellite measurements of CH₄ mixing ratio to estimate the extent of methane emissions. Bangladesh, which comprises most of the Ganges Brahmaputra Meghna (GBM) delta, is a region of particular interest for studying spatio-temporal variation of methane emissions due to (1) broadscale rice cultivation and (2) seasonal flooding and atmospheric convection during the monsoon. Bangladesh and its deltaic landscape exhibit a broad range of environmental, economic, and social circumstances that are relevant to many nations in South and Southeast Asia. We explore the seasonal enhancement of CH₄ in Bangladesh using passive remote sensing spectrometer CH₄ products from the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) and the Atmospheric Infrared Sounder (AIRS). The seasonal variation of CH₄ is compared to independent estimates of seasonal flooding from water gauge stations and space-based passive microwave water-to-land fractions from the Tropical Rainfall Measuring Mission Microwave Imager (TRMM-TMI). Annual cycles in inundation (natural and anthropogenic) and atmospheric CH₄ concentrations show highly correlated seasonal signals. NOAA’s HYSPLIT model is used to determine atmospheric residence time of ground CH₄ fluxes. Using the satellite observations, we can narrow the large uncertainty in extrapolation of ground-based CH₄ emission estimates from rice paddies, allowing for country-wide upscaling of high spatial resolution data. This approach allows for better informed carbon cycling modeling for the GBM delta and is applicable to other regions.