Integrating Multiple Sources of Information for Improving Hydrological Modelling an Ensemble Approach 1st Edition by Isnaeni Murdi Hartanto ISBN 100046346X 9781000468243
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ISBN 10: 100046346X
ISBN 13: 9781000468243
Author: Isnaeni Murdi Hartanto
Integrating Multiple Sources of Information for Improving Hydrological Modelling an Ensemble Approach 1st Table of contents:
Chapter 1. Introduction
1.1 Background
1.2 Motivation
1.3 Research objectives
1.4 Innovation andpractical value
1.5 Terminology
1.6 Thesis outline
Chapter 2. Literature review
2.1 Sources of hydrometeorological data
2.1.1 Ground station data
2.1.2 Earth observation data
2.1.3 Numerical Weather Prediction
2.2 Hydrological models
2.2.1 Models of controlled water systems
2.2.2 SIMGRO modelling system
2.3 Uncertainty in hydrological modelling
2.4 Integration of data and models
2.4.1 Integration of sources of information in hydrological models
2.4.2 Integrating multiple sources of information
2.5 Ensemble Prediction
2.6 Summary and gaps in research
Chapter 3. Methodological framework
3.1 Introduction
3.2 Multi-model ensemble approach
3.3 Data-model integration methods
3.4 Weighting methods
3.5 Performance assessment
3.5.1 Single model performance
3.5.2 Ensemble simulation performance
3.6 Parallel computing as a facilitating technology
3.7 Experimental set-up
Chapter 4. Case study and data sources
4.1 Rijnland
4.1.1 Catchment characteristics
4.1.2 Hydrometeorological data
4.2 Land use data
4.3 Soil data
4.4 Precipitation data
4.5 Evaporation
4.6 Soil moisture and ground water data
4.7 Field survey for EO data calibration in Rijnland
4.8 Summary
Chapter 5. Model development
5.1 Introduction
5.2 SIMGRO model set-up
5.3 SIMGRO model validation
5.3.1 Main Validation
5.3.2 Secondary validation
5.4 Uncertainty analysis
5.5 Summary
Chapter 6. Data-model integration
6.1 Introduction
6.2 Direct use of data in modelparameterisation and simulation
6.2.1 Ground station and radar rainfall
6.2.2 Land-use maps
6.2.3 Soil maps
6.2.4 Summary and discussion
6.3 Merging precipitation data from ground stations and weather radar
6.4 Feeding back the modelled evapotranspiration into EO ETa maps
6.4.1 Data updating and infilling using model result
6.4.2 Data-model updating results
6.4.3 Summary and discussion
6.5 Data assimilation
6.5.1 Particle filter with residual sampling
6.5.2 Data assimilation of EO ETa in Rijnland SIMGRO model
6.5.3 Data assimilation results
6.5.4 Summary and discussion
Chapter 7. Multi-model ensemble
7.1 Introduction
7.2 Constructing the multi-model ensemble
7.3 Ensemble simulation results
7.3.1 Performance individual members and ensemble mean
7.3.2 Performance of the ensemble simulation
7.4 Weighting of ensemble members
7.4.1 Static weighting
7.4.2 Dynamic weighting
7.5 Summary
Chapter 8. Towards implementation in operational systems
Chapter 9. Conclusions and recommendations
9.1 Summary
9.2 Conclusions
9.3 Limitations of this study
9.4 Recommendations
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Isnaeni Murdi Hartanto,Multiple Sources,Improving Hydrological,Ensemble Approach