The inverse problem of estimating the open-channel flow roughness is solved
using an embedded optimization model. Measurement data for flow depths and
discharges at several locations and times are used as inputs to the optimi
zation model. The nonlinear optimization model embeds the finite-difference
approximations of the governing equations for unsteady flow in an open cha
nnel as equality constraints. The Sequential Quadratic Programming Algorith
m is used to solve the optimization model. The performance of the proposed
parameter estimation model is evaluated for different scenarios of data ava
ilability and noise in flow measurement data. Solution results for illustra
tive problems indicate the potential applicability of the proposed model.