Satellite Remote Sensing in Hydrological Data Assimilation

Preface
Summary of Chapters
	Part I—Hydrological Data Assimilation
	Part II—Model-Data
	Part III—Data Assimilation Filters
	Part IV—GRACE Data Assimilation
	Part V—Water Budget Constraint
	Part VI—Data-Driven Approach
	Part VII—Hydrologic Applications
Contents
Part 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
Part II Model-Data
3 Hydrologic Model
	3.1 Background
	3.2 Forcing Observations
4 Remote Sensing for Assimilation
Part 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
Part 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
		6.4.2 Assimilating GRACE Data
	6.5 Results
		6.5.1 Scaling Effect
		6.5.2 Assessment with In-Situ Data
	6.6 Conclusion
Part 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
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
		8.3.3 In-Situ Measurements
	8.4 Methodology
		8.4.1 Problem Formulation
		8.4.2 The Unsupervised Weak Constrained Ensemble Kalman Filter (UWCEnKF)
	8.5 Experimental Setup
		8.5.1 Data Merging
		8.5.2 Data Assimilation
	8.6 Results
		8.6.1 Implementation Results
		8.6.2 Validations with In-Situ Measurements
		8.6.3 Impact of the Equality Constraint
	8.7 Conclusions
Part 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
	9.6 Conclusions
10 Parametric and Non-parametric Data Assimilation Frameworks
	10.1 Summary
	10.2 Introduction
	10.3 Materials
		10.3.1 Data Assimilation (Forecast Step)
		10.3.2 Data Assimilation (Analysis Step)
		10.3.3 Validation Dataset
	10.4 Data Assimilation
		10.4.1 Forecast Step
		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
Part 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
		11.3.3 In-Situ Data
	11.4 Method
		11.4.1 Data Assimilation
		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
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
		12.3.4 Surface Storage Data
		12.3.5 In-Situ Measurements
	12.4 Method
		12.4.1 Data Assimilation
		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
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 Products)
		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
		13.4.2 Water Storage Changes and Climatic Impacts
	13.5 Conclusion
Appendix  Bibliography