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Sonam Dhargay

Candidature

PhD Candidate

Thesis Title

Can LiDAR data help improve ecosystem model predictions for Australia?

Project Summary

Sonam DhargayFuture projections of the terrestrial carbon-balance vary widely over decadal and century timescales due to the existence of complex feedbacks between atmospheric CO2 concentration, climate and the terrestrial carbon cycle. The vegetation dynamics of the terrestrial biosphere thus plays a key role in determining the long-term impact and trajectory of climate change. Reducing errors and sources of uncertainty in dynamic ecosystem models used to represent the carbon cycle is a key global research agenda. Although the use of Light Detection and Ranging (LiDAR) data is widely recognized as having much potential, its utility has not been investigated comprehensively in this domain, especially in the Australian context.

I aim to use Terrestrial Laser Scanning (TLS) and satellite LiDAR (GEDI) data for reducing the sources of uncertainty in a Dynamic Australian Vegetation model (DAVE) which is being developed for Australia. TLS data from field sites across Australia will be used to improve the parameterisation of allometric relationships used in DAVE for simulating plant growth for different Australian plant functional types (PFTs). I will then use TLS data for selected Australian sites to derive tree-architecture based metabolic scaling exponents across rainfall and productivity gradients, which will be used to evaluate the allometric scaling exponents derived from metabolic scaling theory used in the model. Finally I will use a ‘space-for-time’ substitution approach and available GEDI data for the Great Western Woodlands (GWW) in Western Australia (which has been upscaled using airborne LiDAR and plot-level data) to calibrate the demographic parameters of DAVE.

The findings from my research will help to directly constrain and improve allometric and
demographic parameterisation of the DAVE ecosystem model for Australian vegetation; and
indirectly provide additional insights into the environmental controls on metabolic scaling exponents
(particularly rainfall) across Australian ecosystems, which could suggest improvements to the model structure as well.

Supervisor

Distinguished Professor Belinda Medlyn, Professor Benjamin Smith, Professor Matthias Boer, Doctor Assaf Inbar