The main objective of this research is to develop and improve techniques for the processing and assimilation of multi-source remote sensing products into large-scale hydrologic models with the aim to improve current worldwide early-warning systems for droughts. Several sub-objectives are defined:
- to develop a method to obtain a merged and bias-free remote sensing data set for assimilation into hydrologic models and to characterize the uncertainty of the final product.
- to compare the performance of both a physically-based (CLM) and a conceptual (SUPERFLEX) hydrologic model to mimic hydrologic processes at large scale.
- to investigate whether model predictions of soil moisture and discharge can be improved by assimilating different combinations of remote sensing data.
- to assess whether the algorithms and methods developed in this project can be used to improve the existing drought monitoring and early-warning systems.
The techniques will be tested on data from the Murray-Darling basin in southeastern Australia.
