Depth-averaged velocities and unit discharges within a 30 km reach of
one of the world's largest rivers, the Rio Parana, Argentina, were
simulated using three hydrodynamic models with different process
representations: a reduced complexity (RC) model that neglects most of
the physics governing fluid flow, a two-dimensional model based on the
shallow water equations, and a three-dimensional model based on the
Reynolds-averaged Navier-Stokes equations. Row characteristics simulated
using all three models were compared with data obtained by acoustic
Doppler current profiler surveys at four cross sections within the study
reach. This analysis demonstrates that, surprisingly, the performance of
the RC model is generally equal to, and in some instances better than,
that of the physics based models in terms of the statistical agreement
between simulated and measured flow properties. In addition, in contrast
to previous applications of RC models, the present study demonstrates
that the RC model can successfully predict measured flow velocities. The
strong performance of the RC model reflects, in part, the simplicity of
the depth-averaged mean flow patterns within the study reach and the
dominant role of channel-scale topographic features in controlling the
flow dynamics. Moreover, the very low water surface slopes that typify
large sand-bed rivers enable flow depths to be estimated reliably in the
RC model using a simple fixed-lid planar water surface approximation.
This approach overcomes a major problem encountered in the application
of RC models in environments characterised by shallow flows and steep
bed gradients. The RC model is four orders of magnitude faster than the
physics based models when performing steady-state hydrodynamic
calculations. However, the iterative nature of the RC model calculations
implies a reduction in computational efficiency relative to some other
RC models. A further implication of this is that, if used to simulate
channel morphodynamics, the present RC model may offer only a marginal
advantage in terms of computational efficiency over approaches based on
the shallow water equations. These observations illustrate the trade off
between model realism and efficiency that is a key consideration in RC
modelling. Moreover, this outcome highlights a need to rethink the use
of RC morphodynamic models in fluvial geomorphology and to move away
from existing grid-based approaches, such as the popular cellular
automata (CA) models, that remain essentially reductionist in nature. In
the case of the world's largest sand-bed rivers, this might be achieved
by implementing the RC model outlined here as one element within a
hierarchical modelling framework that would enable computationally
efficient simulation of the morphodynamics of large rivers over
millennial time scales. (C) 2012 Elsevier B.V. All rights reserved.