CECOAL   02625
CENTRO DE ECOLOGIA APLICADA DEL LITORAL
Unidad Ejecutora - UE
artículos
Título:
Modelling hydrodynamics in the Rio Paraná, Argentina: An evaluation and inter-comparison of reduced-complexity and physics based models applied to a large sand-bed river
Autor/es:
NICHOLAS, A.; SANDBACH, S.; ASHWORTH, P.; AMSLER, M.; BEST, J.; HARDY, R,; LANE, S,; ORFEO, O.; PARSONS, D.; REESINK, A.; SAMBROOK SMITH, G.; SZUPIANY, R.
Revista:
GEOMORPHOLOGY
Editorial:
ELSEVIER SCIENCE BV
Referencias:
Lugar: Amsterdam; Año: 2012 vol. 169 p. 192 - 211
ISSN:
0169-555X
Resumen:
Depth-averaged velocities and unit discharges within a 30 km reach of one of the world's largest rivers, the
Rio Paraná, Argentina,were simulated using three hydrodynamicmodelswith 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
NavierStokes equations. Flow 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 RCmodels. A further implication of this is
that, if used to simulate channel morphodynamics, the present RCmodel may offer only amarginal 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 morphodynamicmodels in fluvial geomorphology
and tomove away fromexisting 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 RCmodel outlined here as one elementwithin a hierarchicalmodelling framework
that would enable computationally efficient simulation of themorphodynamics of large rivers overmillennial time
scales.