Satellite Remote Sensing in Hydrological Data Assimilation

1. Auflage 2020
ISBN: 978-3-030-37374-0
Verlag: Springer International Publishing

Buch, Englisch, 290 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 629 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Khaki, Mehdi

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Geowissenschaften Geologie Hydrologie, Hydrogeologie
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen
• Geowissenschaften Geologie GIS, Geoinformatik
• Geowissenschaften Geologie Geologie

Inhaltsverzeichnis

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 Ef?cient 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

                6.4 Data Assimilation

                6.4.1 Methods

                6.4.1.1 Square Root Analysis (SQRA)

                6.4.1.2 Filter Tuning

                6.4.2 Assimilating GRACE Data

                6.5 Results

                6.5.1 Scaling Effect

                6.5.2 Assessment with in-situ data

                6.6 Conclusion

V Water Budget Constraint 

7 Constrained Data Assimilation Filtering

                7.1 Summary

                7.2 Introduction

                7.3 Model and Data

                7.3.1 W3RA Hydrological Model

                7.3.2 Terrestrial Water Storage (TWS) Data

                7.3.3 Water Fluxes

                7.3.4 In-situ Measurements

                7.4 The Weak Constrained Ensemble Kalman Filter (WCEnKF)

                7.4.1 Problem Formulation

                7.4.2 The WCEnKF algorithm

7.4.3 Experimental Setup

                7.5 Results

                7.5.1 Error Sensitivity Analysis

                7.5.2 Assessment against In-situ Data

                7.5.3 Water Balance Enforcement

                7.6 Summary and Conclusions

                Acknowledgement

                Appendix A. Some useful properties of random sampling

                Appendix B. Derivation of the WCEnKF algorithm

8 Unsupervised Constraint for Hydrologic Data Assimilation

                8.1 Summary

                8.2 Introduction

                8.3 Model and data

                8.3.1 Hydrological model

                8.3.2 Assimilated observations

                8.3.2.1 Data used in the ?rst update

                8.3.2.2 Data used in the second update

                8.3.3 In-situ measurements

                8.4 Methodology

                8.4.1 Problem formulation

                8.4.2 The Unsupervised Weak Constrained Ensemble Kalman Filter (UW-CEnKF)

                8.4.2.1 The generic algorithm

                8.4.2.2 Practical implementation

                8.5 Experimental setup

                8.5.1 Data merging

                8.5.2 Data assimilation

                8.6 Results

                8.6.1 Implementation results

                8.6.1.1 Iteration impacts

                8.6.1.2 Spatial and temporal balance error variance

                8.6.2 Validations with in-situ measurements

                8.6.3 Impact of the equality constraint

                8.7 Conclusions

                Acknowledgement

VI Data-driven Approach 

9 Non-parametric Hydrologic Data Assimilation

                9.1 Summary

                9.2 Introduction

                9.3 Model and Data

                9.3.1 W3RA

                9.3.2 GRACE TWS

                9.3.3 In-situ measurements

                9.4 Methodology

                9.4.1 Adaptive Unscented Kalman Filter (AUKF)

                9.4.2 Kalman-Takens Method

                9.4.3 Synthetic experiment

                9.4.4 Evaluation metrics

                9.5 Results

                9.5.1 Synthetic experiment

                9.5.2 Assessment with in-situ data

                9.5.3 Assessing the performance of AUKF and Kalman-Taken ?lters

                9.5.3.1 Filters ef?ciency

                9.5.3.2 Water storage update

                9.6 Conclusions

                Acknowledgement

10 Parametric and Non-parametric Data Assimilation Frameworks

                10.1 Summary

                10.2 Introduction

                10.3 Materials

                10.3.1 Data assimilation (forecast step)

                10.3.1.1 W3RA

                10.3.2 Data assimilation (analysis step)

                10.3.2.1 GRACE TWS

                10.3.2.2 Soil Moisture

                10.3.3 Validation dataset

                10.3.3.1 Water Fluxes

                10.3.3.2 In-situ data

                10.4 Data Assimilation

                10.4.1 Forecast step

                10.4.1.1 SQRA

                10.4.1.2 The Kalman-Takens

                10.4.2 Analysis step

                10.4.3 Filter Implementation

                10.5 Results

                10.5.1 Groundwater evaluation

                10.5.2 Soil moisture evaluation

                10.5.3 Water ?uxes assessment

                10.6 Discussion

                10.7 Conclusion

VII Hydrologic Applications 

11 Groundwater Depletion over Iran

                11.1 Summary

                11.2 Introduction

                11.3 Study area and data

                11.3.1 Iran

                11.3.2 W3RA hydrological model

                11.3.2.1 Satellite-derived observations

                11.3.2.2 Temperature

                11.3.3 In-situ data

                11.4 Method

                11.4.1 Data assimilation

                11.4.1.1 EnSRF ?ltering

                11.4.1.2 Assimilating GRACE TWS into W3RA

                11.4.2 Canonical Correlation Analysis (CCA)

                11.5 Results and discussion

                11.5.1 Simulated assimilation

                11.5.2 Result evaluation

                11.5.3 Water storage analysis

                11.5.4 Climatic impacts

                11.5.5 CCA results

                11.6 Conclusions

                Acknowledgement

12 Water Storage Variations over Bangladesh

                12.1 Summary

                12.2 Introduction

                12.3 Study Area and Data

                12.3.1 Bangladesh

                12.3.2 W3RA Hydrological Model

                12.3.3 Remotely Sensed Observations

                12.3.3.1 GRACE

                12.3.3.2 Satellite Radar Altimetry

                12.3.3.3 Precipitation

                12.3.4 Surface Storage Data

                12.3.5 In-situ measurements

                12.4 Method

                12.4.1 Data Assimilation

                12.4.1.1 Filtering Method

                12.4.1.2 Assimilation of GRACE data

                12.4.2 Empirical Mode Decomposition (EMD)

                12.4.3 Retracking Scheme

                12.4.4 Canonical Correlation Analysis (CCA)

                12.5 Results

                12.5.1 Data Assimilation

                12.5.2 Statistical Analyses

                12.6 Conclusion

                Acknowledgement

13 Multi-mission Satellite Data Assimilation over South America

                13.1 Summary

                13.2 Introduction

                13.3 Materials and methods

                13.3.1 W3RA hydrological model

                13.3.2 Remotely sensed observations (GRACE, soil moisture and TRMM prod-ucts)

                13.3.2.1 GRACE TWS

                13.3.2.2 Satellite soil moisture

                13.3.2.3 Precipitation

                13.3.3 Surface storage data

                13.3.4 In-situ groundwater measurements

                13.3.5 Data assimilation ?ltering method

                13.3.6 Experimental setup

                13.3.7 Climate variability impacts

                13.4 Results and discussions

                13.4.1 Data assimilation

                13.4.1.1 Observation impacts on state variables

                13.4.1.2 Evaluation results

                13.4.2 Water storage changes and climatic impacts

                13.5 Conclusion

                Acknowledgement

                Bibliography

Über Autor(innen)

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.