Description
Taylor & Francis Ltd Recursive Streamflow Forecasting 2010 Edition by Jozsef Szilagyi, Andras Szollosi Nagy
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 (R) codes for the simulation of streamflows and the creation of real-time hydrological forecasts. 1. Introduction2. Overview of continuous flow routing techniques2.1. Basic equations of the one-dimensional, gradually varied nonpermanent open channel flow2.2. Diffusion wave equation2.3. Kinematic wave equation2.4. Flow routing methods2.4.1. Derivation of the storage equation from the Saint-Venant equations2.4.2. The Kalinin-Milyukov-Nash cascade2.4.3. The Muskingum channel routing technique3. State-space description of the spatially discretized linear kinematic wave3.1. State-space formulation of the continuous, spatially discrete linear kinematic wave3.2. Impulse response of the continuous, spatially discrete linear kinematic wave4. State-space description of the continuous Kalinin-Milyukov-Nash (KMN) cascade4.1. State equation of the continuous KMN-cascade4.2. Impulse response of the continuous KMN-cascade and its equivalence with the continuous, spatially discrete linear kinematic wave4.3. Continuity, steady state, and transitivity of the KMN-cascade5. State-space description of the discrete linear cascade model (DLCM) and its properties: The pulse-data system approach5.1. Trivial discretization of the continuous KMN-cascade and its consequences5.2. A conditionally adequate discrete model of the continuous KMNcascade5.2.1. Derivation of the discrete cascade, its continuity, steady state and transitivity5.2.2. Relationship between conditionally adequate discrete models with different sampling intervals5.2.3. Temporal discretization and numerical diffusion5.3. Deterministic prediction of the state variables of the discrete cascade using a linear transformation5.4. Calculation of system characteristics5.4.1. Unit-pulse response of the discrete cascade5.4.2. Unit-step response of the discrete cascade5.5. Calculation of initial conditions for the discrete cascade5.6. Deterministic prediction of the discrete cascade output and its asymptotic behavior5.7. The inverse of prediction: input detection6. The sample-data system approach6.1. Formulation of the discrete cascade in a sample-data system framework6.2. Discrete state-space approximation of the continuous KMN-cascade of noninteger storage elements6.3. Application of the discrete cascade for flow routing with unknown rating curves7. DLCM and stream-aquifer interactions7.1. Accounting for stream-aquifer interactions in DLCM7.2. Assessing groundwater contribution to the channel via input detection8. Handling of model-error: the deterministic-stochastic model and its prediction updating8.1. A stochastic model of forecast errors8.2. Recursive prediction and updating9. Some practical aspects of model application for real-time operational forecasting9.1. Model parameterization9.2. Comparison of a pure stochastic, deterministic (DLCM), and the deterministic-stochastic models9.3. Application of the deterministic-stochastic model for the Danube basin in Hungary10. Summary11. Appendix11.1. State-space description of linear dynamic systems11.2. Algorithm of the discrete linear Kalman filter12. References13. Guide to the exercises