I Hydrological Data Assimilation
1 Introduction
1.1 Hydrologic modelling, challenges and opportunities
1.2 Data assimilation
1.3 Hydrological data assimilation
2 Data assimilation and remote sensing data
2.1 Satellite remote sensing, new opportunities
2.2 Satellite data assimilation challenges
II Model-Data 14
3 Hydrologic model
3.1 Background
3.2 Forcing observations
4 Remote sensing for assimilation
III Data Assimilation Filters
5 Sequential Data Assimilation Techniques for Data Assimilation 5.1 Summary
5.2 Introduction
5.3 Model and Datasets
5.3.1 W3RA
5.3.2 GRACE-derived Terrestrial Water Storage
5.3.3 In-situ data 5.4 Filtering Methods and Implementation
5.4.1 Stochastic Ensemble Kalman Filter (EnKF)
5.4.2 Deterministic Ensemble Kalman Filters
5.4.3 Particle Filtering
5.4.4 Filter Implementation
5.5 Results
5.5.1 Assessment with GRACE and in-situ data
5.5.2 Error Analysis
5.6 Summary and Conclusions
IV GRACE Data Assimilation 6 Efficient Assimilation of GRACE TWS into Hydrological Models
6.1 Summary
6.2 Introduction
6.3 Datasets
6.3.1 GRACE
6.3.2 W3RA
6.3.3 Validation Data
About the Author:
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.
