Longitudinal interpolation of parameters characterizing channel geometry by piece-wise polynomial and universal kriging methods: effect on flow modeling

Channel geometry often is described by a set of longitudinally varying parameters measured at a set of survey stations. To support flow modeling at arbitrary resolution, three methods of parameter interpolation are described including piece-wise linear interpolation, monotone piece-wise-cubic Hermitian interpolation, and universal kriging. The latter gives parameter estimates that minimize the mean square error of the interpolator, and therefore can be used as a standard against which the accuracy of polynomial methods can be assessed. Based on the application of these methods to a dataset describing cross-sectional properties at 283 stations, piece-wise linear interpolation gives parameter estimates that closely track universal kriging estimates and therefore this method is recommended for routine modeling purposes. Piece-wise-cubic interpolation gives parameter estimates that do not track as well. Differences between cubic and kriging estimates were found to be 2–10 times larger than differences between linear and kriging parameter estimates. In the context of one-dimensional flow modeling, the sensitivity of steady state water level predictions to the channel bed interpolator is comparable to a 5% change in the Manning coefficient.