Satellite remote sensing in hydrological data assimilation

著者

    • Khaki, Mehdi

書誌事項

Satellite remote sensing in hydrological data assimilation

Mehdi Khaki

Springer, c2020

大学図書館所蔵 件 / 全2件

この図書・雑誌をさがす

注記

Bibliography: p. 263-290

内容説明・目次

内容説明

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.

目次

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 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 first 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 filters 9.5.3.1 Filters efficiency 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 fluxes 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 filtering 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 filtering 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

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