Recursive Streamflow Forecasting : A State Space Approach (Paperback)

1. Introduction

2. Overview of continuous flow routing techniques

• 2.1. Basic equations of the one-dimensional, gradually varied nonpermanent open channel flow
• 2.2. Diffusion wave equation
• 2.3. Kinematic wave equation
• 2.4. Flow routing methods
• 2.4.1. Derivation of the storage equation from the Saint-Venant equations
• 2.4.2. The Kalinin-Milyukov-Nash cascade
• 2.4.3. The Muskingum channel routing technique

3. State-space description of the spatially discretized linear kinematic wave

• 3.1. State-space formulation of the continuous, spatially discrete linear kinematic wave
• 3.2. Impulse response of the continuous, spatially discrete linear kinematic wave

4. State-space description of the continuous Kalinin-Milyukov-Nash (KMN) cascade

• 4.1. State equation of the continuous KMN-cascade
• 4.2. Impulse response of the continuous KMN-cascade and its equivalence with the continuous, spatially discrete linear kinematic wave
• 4.3. Continuity, steady state, and transitivity of the KMN-cascade

5. State-space description of the discrete linear cascade model (DLCM) and its properties: The pulse-data system approach

• 5.1. Trivial discretization of the continuous KMN-cascade and its consequences
• 5.2. A conditionally adequate discrete model of the continuous KMNcascade
• 5.2.1. Derivation of the discrete cascade, its continuity, steady state and transitivity
• 5.2.2. Relationship between conditionally adequate discrete models with different sampling intervals
• 5.2.3. Temporal discretization and numerical diffusion
• 5.3. Deterministic prediction of the state variables of the discrete cascade using a linear transformation
• 5.4. Calculation of system characteristics
• 5.4.1. Unit-pulse response of the discrete cascade
• 5.4.2. Unit-step response of the discrete cascade
• 5.5. Calculation of initial conditions for the discrete cascade
• 5.6. Deterministic prediction of the discrete cascade output and its asymptotic behavior
• 5.7. The inverse of prediction: input detection

6. The sample-data system approach

• 6.1. Formulation of the discrete cascade in a sample-data system framework
• 6.2. Discrete state-space approximation of the continuous KMN-cascade of noninteger storage elements
• 6.3. Application of the discrete cascade for flow routing with unknown rating curves

7. DLCM and stream-aquifer interactions

• 7.1. Accounting for stream-aquifer interactions in DLCM
• 7.2. Assessing groundwater contribution to the channel via input detection

8. Handling of model-error: the deterministic-stochastic model and its prediction updating

• 8.1. A stochastic model of forecast errors
• 8.2. Recursive prediction and updating

9. Some practical aspects of model application for real-time operational forecasting

• 9.1. Model parameterization
• 9.2. Comparison of a pure stochastic, deterministic (DLCM), and the deterministic-stochastic models
• 9.3. Application of the deterministic-stochastic model for the Danube basin in Hungary

10. Summary

11. Appendix

• 11.1. State-space description of linear dynamic systems
• 11.2. Algorithm of the discrete linear Kalman filter

12. References

13. Guide to the exercises