Satellite Remote Sensing in Hydrological Data Assimilation

This book presents the fundamentals of data assimilation and reviews the application of satellite remote sensing in hydrological data assimilation. Although hydrological models are valuable tools to monitor and understand global and regional water cycles, they are subject to various sources of errors. Satellite remote sensing data provides a great opportunity to improve the performance of models through data assimilation.

Autorentext

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.

Inhalt

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 Efcient Assimilation of GRACE TWS into Hydrological Models &...