Jiangong Liu: Methane emissions from an estuarine mangrove and a freshwater fishpond in Hong Kong

The role of coastal mangrove wetlands in sequestering atmospheric CO2 and mitigating climate change has received increasing attention in recent years. On the other hand, mangroves have experienced extensive deforestation, which was predominantly attributable to the expansion of aquaculture. Aquacultural ponds are hotspots of CH4 emissions, while the mangrove CH4 emission largely depends on local hydrology. Here we examined the temporal variations and multi-scale biophysical drivers of ecosystem-scale CH4 fluxes in a subtropical estuarine mangrove wetland and a commercial fishpond in Hong Kong based on multiple years of eddy covariance measurements. Because mangroves and fishponds are complex systems subject to periodic and irregular changes in meteorology, tides, water quality and biological factors, we used a combination of singular spectrum analysis, information theory and interpretable machine learning to inspect the dominant drivers of CH4 fluxes. We found that the mangrove wetland was a moderate CH4 source, emitting ~12 gC- m-2 yr-1, while the fishpond is a strong CH4 source, emitting ~102 gC m-2 yr-1. Plant activities and physical variables (such as soil temperature and salinity) dominated the short-term and long-term variability in the mangrove CH4 flux through synchronous and asynchronous processes, respectively. Temperature dominantly controls the fishpond CH4 dynamics. Finally, we quantified that the CO2-equivalent loss from aquacultural ponds can be four times that of the foregone carbon sequestration potential caused by mangrove loss, highlighting the importance of greenhouse gas quantification from the subsequent system after deforestation.

Bio

Jiangong Liu is a postdoctoral research scientist in the Department of Earth and Environmental Engineering at Columbia University. He obtained his PhD in physical geography at The Chinese University of Hong Kong in 2020. Jiangong is interested in wetland biogeochemistry, plant photosynthetic physiology, and land-atmosphere interactions. His current work is focused on better understanding and modeling terrestrial carbon and water fluxes using machine learning models.