Critical perspectives on the evaluation and optimization of complex numerical models of estuary hydrodynamics and sediment dynamics

Abstract

Numerical hydrodynamic and sediment transport models provide a means of extending inferences from direct observation and for advancing our understanding of estuarine processes. However, their parametric complexity invites questions concerning the extent to which model output can be assessed with respect to data. This paper examines the basis for evaluating the performance of complex hydrodynamic and sediment transport models, with reference to a case study of a muddy meso-tidal estuary. Sophisticated and computationally-intensive models should be evaluated using robust objective functions, but conventional measures of fi t and model effi ciency invoke restrictive assumptions about the nature of the errors. Furthermore, they offer little insight into causes of poor performance. Optimization of tidal hydrodynamic models can usefully combine conventional performance measures with harmonic analysis of modelled shallow water tidal constituents that are diagnostic of the interactions between tidal propagation, bathymetry and bottom friction. Models with similar effi ciencies can thus be distinguished and likely sources of error pinpointed. Hydrodynamic models have a predictive power that is rooted in a more-or-less complete representation of the physical processes and boundary conditions that are well-constrained with respect to data. In contrast, fi ne sediment models rely on a less complete conceptualization of a broader set of processes and, crucially, have a parametric complexity that is unmatched by the quantity and quality of observational data. Their performance as measured by conventional objective functions is weaker and it is important to match the structural complexity of model errors with analyses that can localize the scales and times of poor performance. Wavelet analysis is potentially useful here as a means of identifying aspects of the model that need improvement. The context in which such models are deployed is also important. Used heuristically, what might otherwise be dismissed as weak models can still provide mechanistic support for empirically-derived inferences concerning specifi c aspects of system behaviour.

Formation and structure of the turbidity maximum in the macrotidal Charente estuary (France): Influence of fluvial and tidal forcing

“…Concerning sediment dynamics, French (2010) noted that it was difficult to validate modelled suspended sediment concentrations, compared to tidal levels and velocities. This difficulty arises from the complexity inherent in the large parameter sets of suspended sediment models.…”

Surveyed and modelled one‐year morphodynamics in the braided lower Tana River

“…B s c is the surveyed elevation and B m c is the modelled bed elevation at cell c, while M is the mean surveyed elevation (Nash and Sutcliffe, 1970;Krause et al, 2005). The Nash-Sutcliffe efficiency coefficient has normally been applied to hydrological and hydraulic rather than morphodynamic simulations (Moriasi et al, 2007;French, 2010). Nevertheless, the Nash-Sutcliffe index has also been used to calibrate the critical shear stress with the help of predicted and surveyed scour depths, while applications for suspended sediment models also exist (Wells et al, 2009;French, 2010).…”

“…The Nash-Sutcliffe efficiency coefficient has normally been applied to hydrological and hydraulic rather than morphodynamic simulations (Moriasi et al, 2007;French, 2010). Nevertheless, the Nash-Sutcliffe index has also been used to calibrate the critical shear stress with the help of predicted and surveyed scour depths, while applications for suspended sediment models also exist (Wells et al, 2009;French, 2010). Its use alone has been considered by some authors to be inadequate for describing the model's performance (Jain and Sudheer, 2008).…”

Nationwide Digital Terrain Models for Topographic Depression Modelling in Detection of Flood Detention Areas

“…NSE ranges between −∞ and 1.0, in which NSEs from 0.0 to 1.0 are acceptable levels of performance, NSE 1.0 is the optimum and NSE < 0.0 means unacceptable performance in which the mean value of surveyed data is a better predictor than the simulated values. It has been applied in hydrologic and hydraulic simulations [58,59], but it has also been used to describe the predictive accuracy of other models, like suspended sediment and morphodynamic models [60,61].…”