Forrest M. Holly

State space model for river temperature prediction

A stochastic state space model for the estimation of fiver temperature is presented, setting the stage for its implementation in optimal control algorithms. Physical processes modeled include advection, diffusion, and environmental heat exchange; the mathematical formulation is one dimensional. The deterministic formulation uses a hybrid characteristics-finite differences numerical scheme for the solution of the governing equations. The stochastic formulation accounts for uncertainty due to model assumptions, errors in the model inputs and parameters, and fiver temperature measurements. Model use is demonstrated by the estimation of temperatures in a 5-mile (8 kin) reach of the Des Plaines River, Illinois, below the Joliet power station. River temperature measurements and hydrological and meteorological data are used to estimate the model parameters; model formulation and limitations are assessed based on the model application results. The stochastic formulation improves river temperature estimation, as measured by the mean, variance-covariance, correlation, and range of the residuals.

Field observations of longitudinal dispersion in a run-of-the-river impoundment

Abstract Dye studies were conducted on a run-of-the-river impoundment to observe longitudinal dispersion in a lacustrine/riverine environment. Field data were analyzed in the context of a streamtube model which accounts for dispersion by the lateral distribution of longitudinal velocity. The differential advection associated with the vertical distribution of longitudinal velocity [i.e. Elders equation ( D x = α x u ∗ h )] is incorporated in this stream-tube model providing longitudinal dispersion within each tube. Model calibration attempts required α values on the order of 100, greatly in excess values expected from fluvial experience. A diffusive stall was observed twice when the dye plume moved into a sudden expansion of channel width. The stall is predicted by Fischers K x equation. The stream-tube model reasonably reproduced the field data in a variety of initial-period conditions, caused by the highly variable geometry of Coralville Reservoir.