Satellite Remote Sensing in Hydrological Data Assimilation

✍ Scribed by Mehdi Khaki

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 292
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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