1st Edition
Recursive Streamflow Forecasting A State Space Approach
This textbook is a practical guide to real-time streamflow forecasting that provides a rigorous description of a coupled stochastic and physically based flow routing method and its practical applications. This method is used in current times of record-breaking floods to forecast flood levels by various hydrological forecasting services. By knowing in advance when, where, and at what level a river will crest, appropriate protection works can be organized, reducing casualties and property damage. Through its real-life case examples and problem listings, the book teaches hydrology and civil engineering students and water-resources practitioners the physical forecasting model and allows them to apply it directly in real-life problems of streamflow simulation and forecasting. Designed as a textbook for courses on hydroinformatics and water management, it includes exercises and a CD-ROM with MATLAB® codes for the simulation of streamflows and the creation of real-time hydrological forecasts.
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
Biography
Jozsef Szilagyi, Andras Szollosi Nagy
