Khaki, Dr Mehdi1
1University Of Newcastle, Newcastle, Australia
Hydrological models are also crucial for simulating water storage and water fluxes. They reflect our knowledge of hydrological processes and are often used to study the impact of climate variability and changes on the Earth system. The ability of the models to accurately simulate and more importantly (reliably) predict water storage and fluxes highly depends on their parameters. This study applies an innovative sequential data assimilation technique to enhance a hydrological model’s simulations using multivariate satellite remote sensing while calibrating its parameters. The assimilation-calibration approach is based on the recently proposed data assimilation method, the Unsupervised Weak Constrained Ensemble Kalman Filter (UWCEnKF), extended to calibrate model parameters simultaneously with the state. The filtering method applies the joint state-parameter filtering problem, which results in a dual-type filtering scheme separately updating the state and parameters using two interactive Ensemble Kalman Filters (EnKFs). Numerical experiments are designed over globally distributed basins to test the proposed approach over two periods: calibration from 2003 to 2013 and prediction from 2013 to 2016. Results indicate that simultaneous assimilation of multiple satellite products combined with parameter estimation improves model predictions compared with single satellite products and/or state estimation alone. This improvement is achieved during both experiment periods, e.g., ~32% groundwater RMSE reduction over the calibration period and soil moisture correlation increase from ~0.69 to ~0.78 during the prediction period.
After my PhD graduation in 2018 from Curtin University, I started working as a lecturer at the University of Newcastle (Australia). My research is centred about the application of geodetic and remote sensing techniques and their integration with available models to improve their simulations at various scales.