Impact of operational model nesting approaches and inherent errors for coastal simulations

Abstract

A region of freshwater influence (ROFI) under hypertidal conditions is used to demonstrate inherent problems for nested operational modelling systems. Such problems can impact the accurate simulation of freshwater export within shelf seas, so must be considered in coastal ocean modelling studies. In Liverpool Bay (our UK study site), freshwater inflow from 3 large estuaries forms a coastal front that moves in response to tides and winds. The cyclic occurrence of stratification and remixing is important for the biogeochemical cycles, as nutrient and pollutant loaded freshwater is introduced into the coastal system. Validation methods, using coastal observations from fixed moorings and cruise transects, are used to assess the simulation of the ROFI, through improved spatial structure and temporal variability of the front, as guidance for best practise model setup. A structured modelling system using a 180 m grid nested within a 1.8 km grid demonstrates how compensation for error at the coarser resolution can have an adverse impact on the nested, high resolution application. Using 2008, a year of typical calm and stormy periods with variable river influence, the sensitivities of the ROFI dynamics to initial and boundary conditions are investigated. It is shown that accurate representation of the initial water column structure is important at the regional scale and that the boundary conditions are most important at the coastal scale. Although increased grid resolution captures the frontal structure, the accuracy in frontal position is determined by the offshore boundary conditions and therefore the accuracy of the coarser regional model.

The Evolution of Plume Fronts in the Rhine Region of Freshwater Influence

“…The agreement shown by the metrics in Table 1 is typical of ROFIs. The bias and RSME errors result from initialization fields, wind fields and because the sources and variability of freshwater are not well resolved within the input data (Brown et al., 2016). In the case of the velocity, this is due to the fact that we are not using all of the tidal components, together with the interactions with salinity.…”

OceanMesh2D 1.0: MATLAB-based software for two-dimensional unstructured mesh generation in coastal ocean modeling

“…Improvement strategies one and two add and delete vertices when they are part of edges that are too long and short, which produces a set of new edges that more closely approximate the mesh size function. Improvement strategy three directly reduces the occurrence of low vertex-to-vertex connectivity (valency of three or four), for which a valency of six is ideal (Canann et al, 1993). Note that improvement strategy one also helps to reduce high vertex-to-vertex connectivity indirectly by avoiding steep transitions in the element size at which valencies greater than six tend to develop.…”

Uncertainty in estuarine extreme water level predictions due to surge-tide interaction

“…These tide gauge locations are spaced throughout the estuary, and data are freely available to download from the British Oceanography Data Centre, with uninterrupted observational records available for the extreme water level events selected. Widely used error metrics (R 2 [ 54 ], NRMSE [ 51 , 55 ], Bias of the maximum value [ 55 , 56 ]) are calculated at tide gauge locations up-estuary. These metrics confirm that the model can reproduce observational tide gauge data without the inclusion of meteorological forcing and waves and can be used to assess the error introduced by this methodology.…”