Current MDI Projects
Other projects
Analysis of climate constraints on gross primary productivity based on light use efficiency models: Shanning analyzes the patterns of climate sensitivities of gross primary productivity using FLUXNET data and global sun-induced fluorescence data. The project focuses on the evaluation and prediction of climate sensitivity functions and model parameters based on model-data fusion strategy.
Ecosystem transpiration from eddy covariance, where Jake develops and assesses methods for partitioning the evapotranspiration flux measured from eddy covariance into the transpiration and abiotic evaporation components. The methods are applied to the FLUXNET dataset, giving a global perspective on plant water use.
Ecological process understanding across time scales: In her PhD project, Nora explores time-scale dependencies between vegetation and climate globally, to answer questions such as: How does the decadal sensitivity of vegetation to climate differ from seasonal sensitivity? Can we extrapolate atmosphere-biosphere relations from one time scale to the other? Where and when do we need to account for such time-scale dependencies in vegetation modeling and monitoring?
Water-carbon-cycle interactions across scales: In his PhD project, Hoontaek attempts to answer 1) how interactions between water and carbon cycles form observations of each cycle and 2) who drives whom at which scales. To do so, Hoontaek will take advantage of the stream of global datasets and a model-data-fusion framework.
Change in global vegetation: Here Siyuan explores data-assimilation to study vegetation processes and ecosystem responses to environment at different spatio-temporal scales. The project investigates the links between ecosystem carbon fluxes and changes in vegetation carbon states, integrating vegetation mortality and metabolic activity based on a novel modular modeling framework and different observational data streams.
Inter-annual variability in terrestrial ecosystem fluxes, in this PhD project, Ranit endeavours to develop a hybrid modelling approach to simulate water and carbon fluxes at the ecosystem level. The project will exploit large multi-variate Earth Observation datasets to explore causal linkages between changing climate and ecosystem responses by taking advantage of both physically based models (physically interpretable) and machine learning approaches (data-adaptive)