Preface p. xi
Introduction p. 1
Overview of Continuous Flow-Routing Techniques p. 7
Basic equations of the one-dimensional, gradually varied non-permanent open-channel flow p. 8
Diffusion wave equation p. 10
Kinematic wave equation p. 12
Flow-routing methods p. 12
Derivation of the storage equation from the Saint-Venant equations p. 13
The Kalinin-Milyukov-Nash cascade p. 14
The Muskingum channel routing technique p. 16
State-Space Description of the Spatially Discretized Linear Kinematic Wave p. 19
State-space formulation of the continuous, spatially discrete linear kinematic wave p. 19
Impulse response of the continuous, spatially discrete linear kinematic wave p. 22
State-Space Description of the Continuous Kalinin-Milyukov-Nash (KMN) Cascade p. 31
State equation of the continuous KMN-cascade p. 31
Impulse-response of the continuous KMN-cascade and its equivalence with the continuous, spatially discrete, linear kinematic wave p. 33
Continuity, steady state, and transitivity of the KMN-cascade p. 35
State-Space Description of the Discrete Linear Cascade Model (DLCM) and its Properties: The Pulse-Data System Approach p. 39
Trivial discretization of the continuous KMN-cascade and its consequences p. 40
A conditionally adequate discrete model of the continuous KMN-cascade p. 45
Derivation of the discrete cascade, its continuity, steady state, and transitivity p. 46
Relationship between conditionally adequate discrete models with different sampling intervals p. 54
Temporal discretization and numerical diffusion p. 57
Deterministic prediction of the state variables of the discrete cascade using a linear transformation p. 60
Calculation of system characteristics p. 62
Unit-pulse response of the discrete cascade p. 63
Unit-step response of the discrete cascade p. 69
Calculation of initial conditions for the discrete cascade p. 72
Deterministic predication of the discrete cascade output and its asymptotic behavior p. 77
The inverse of prediction: input detection p. 78
The Linear Interpolation (LI) Data System Approach p. 87
Formulation of the discrete cascade in the LI-data system framework p. 87
Discrete state-space approximation of the continuous KMN-cascade of noninteger storage elements p. 97
Application of the discrete cascade for flow-routing with unknown rating curves p. 102
Detecting historical channel flow changes by the discrete linear cascade p. 106
DLCM And Stream-Aquifer Interactions p. 109
Accounting for stream-aquifer interactions in DLCM p. 109
Assessing groundwater contribution to the channel via input detection p. 116
Handling of Model Error: The Deterministic-Stochastic Model and its Prediction Updating p. 119
A stochastic model of forecast errors p. 119
Recursive predication and updating p. 122
Some Practical Aspects of Model Application for Real-Time Operational Forecasting p. 133
Model parameterization p. 133
Comparison of a pure stochastic, a deterministic (DLCM), and deterministic-stochastic models p. 134
Application of the deterministic-stochastic model for the Danube basin in Hungary p. 138
Summary p. 141
Appendix I p. 145
State-space description of linear dynamic systems p. 145
Algorithm of the discrete linear Kalman filter p. 148
Appendix II p. 159
Sample MATLAB scripts p. 159
References p. 181
Guide to the Exercises p. 187
Subject Index p. 191