Title: Parameter optimization for land surface models in simulation of plant-atmosphere exchange of water and carbon dioxide
Supervisors: Professor Qiang Yu (UTS), Dr. Ying Ping Wang (principle research scientist, CSIRO)
(1) The land surface models
Land surface models represent soil-vegetation-atmosphere interactions, linking physical, chemical, and biological processes between terrestrial ecosystem and atmosphere. These models have been incorporated with understanding from field measurements, which provide model parameters and processes parameterization.
(2) The model uncertainty and improvement
• Land surface modeling is subject to errors from meteorological forcing data, parameterization schemes, and model parameters.
• Precisions of model prediction are determined by precision of meteorological data, and model parameter, as well as soundness of modelled processes schemes, which results in issue of model uncertainty.
• Methodology of statistics, including theory and software (e.g. Lingo), can be applied to explore suitability of model complexity and resolution of measurement.
The research objective of this project is to investigate uncertainty of land surface models in simulation of terrestrial CO2, H2O and energy budget, and characteristics of plant functional types (PFTs)
(1) Methodology of parameter optimization, and datasets of vegetation parameters for research of climate change and land surface-atmosphere exchange;
(2) Quantification of climate change impacts on terrestrial carbon sequestration and water balance;
(3) One or two publications in peer reviewed journals.
(1) Improve understanding of interaction between climate change and plant function
(2) Further development of CO2 and H2O flux models in response to extreme weather
Ying-Ping Wang, Cathy M. Trudinger, Ian G. Enting, 2009. A review of applications of model–data fusion to studies of terrestrial carbon fluxes at different scales. Agricultural and Forest Meteorology 149: 1829–1842.