I have been working on two research projects. One is to quantify the role of water in regulating global ecosystem state and function, combining both the carbon and water cycles. Another is to explore the application of machine learning-based emulator on improveing the current processed-based groundwater models.
SMAP: Soil moisture controls on global vegetation productivity
The objective of this project is to improve understanding of the global exchange of carbon between
terrestrial ecosystems and the atmosphere,by utilizing new theory and observations in land, atmospheric and space-based research
at high spatial and temporal resolutions. More details here.
Some preliminary results:
Emulator for Groundwater Model
The objective of this project is to develop machine learning based emulator to help conduct uncertainty analysis and sensitivity analysis of
numerical groundwater model with reduced computation cost.
Some preliminary results:
