I use models and observations to understand the terrestrial carbon cycle through model-data fusion, benchmarking and skilled-based multi-model averaging. My aim is to better quantify modern land-atmosphere carbon fluxes and the evolution of biomass stocks to identify likely terrestrial sources and sinks of atmospheric carbon dioxide. This knowledge can inform the development of robust projections of the land surface potential to be used to mitigate climate change.
Due to my background in hydrological sciences, I am also involved in studies on the impact of climate change on agriculture, and looking at climate change and human pressure impacts on water resources in river basins worldwide. I have a keen interest in innovative numerical methods based on machine-learning techniques to explore large observational datasets.