Satellite Remote Sensing in Hydrological Data Assimilation

Dr. Mehdi Khaki received his Bachelor of Civil Engineering in Surveying from the University of Tehran (Iran) in 2011. He also holds an M.Sc. in Geodesy from the same institute (2014), and a PhD in Spatial Sciences from Curtin University (Australia). In 2018, he started working as a lecturer at the School of Engineering, at the University of Newcastle (Australia). Mehdi's research focuses on the application of geodetic and remote sensing techniques and their integration with hydrological models to improve their simulations in various spatial scales. He has developed new satellite data filtering techniques to improve their quality and also new data assimilation methods for integrating multiple satellite-derived measurements, e.g. satellite gravity and soil moisture measurements with hydrologic models. Using these he was able to analyse water storage and its variations, as well as its connection with the anthropogenic and climatic impacts in various parts of the world, such as Australia, Iran, Bangladesh, South America and Africa.

Part 1: Hydrological Data Assimilation.- Chapter 1 -  Introduction.- Chapter 2 - Data assimilation and remote sensing data.- Part 2: Model-Data.- Chapter 3 - Hydrologic model.- Chapter 4 - Remote sensing for assimilation.- Part 3 : Data Assimilation Filters.- Chapter 5 - Sequential Data Assimilation Techniques for Data Assimilation.- Part 4 : GRACE Data Assimilation.- Chapter 6 - Efficient Assimilation of GRACE TWS into Hydrological Models.- Part 5 : Water Budget Constraint.- Chapter 7 - Constrained Data Assimilation Filtering.-  Chapter 8 - Unsupervised Constraint for Hydrologic Data Assimilation.- Part 6 : Data-driven Approach.- Chapter 9 -  Non-parametric Hydrologic Data Assimilation.- Chapter 10 -  Parametric and Non-parametric Data Assimilation Frameworks.- Part 7 Hydrologic Applications.- Chapter 11- Groundwater Depletion over Iran.- Chapter 12 - Water Storage Variations over Bangladesh.- Chapter 13 - Multi-mission Satellite Data Assimilation over South America.