Additional Functionality

This section describes the new functionality that has been implemented within the GIS. Each element is considered in turn with an explanation of the rationale behind its development and a description of the way it has been implemented. A full assessment of the usefulness of each of these additions is described in Chapter 6.

The additional functions allow analysis in two main areas: • Analysis of inshore wave height.

• Analysis of seabed morphology.

3.5.1 Inshore Wave Height Analysis Tools

The IWMS can generate inshore wave data for multiple inshore points. These wave conditions are imported back into the GIS for visualisation. The generation of more data does not automatically guarantee the return of better quality information to managers. Model results need to be analysed to help explain how they impact on the coastline and

how this relates to modelling and management strategies that may subsequently be implemented.

The inshore wave height analysis tools are designed to query tables of model results in the GIS. They all query the inshore wave height. This could easily be amended if inshore wave direction for example was considered more important for a particular study. The most effective analysis is achieved using inshore points that have been positioned along a specific inshore contour as their constant depth ensures that results are comparable.

The variation in inshore wave height along a contour identifies whether wave conditions are similar for many miles along the coast, or whether local phenomena generate small areas where waves are generally much higher or lower than surrounding areas. Identification of areas such as these can be of use for many coastal management issues including:

• Erosion and accretion studies.

• Calculating the required strength and positioning of coastal defences. • Nomination of areas for conservation or leisure activities.

• Siting of new developments for housing, leisure or industry.

Another issue is the efficiency of modelling techniques. If the inshore wave height can be analysed to assess the distance along the coastline over which it remains very similar, then this allows an assessment of how many inshore points are needed to fully describe the wave climate along it. This information can then be used for all subsequent studies.

The tools developed within the IWMS allow the user to approach this issue of the spatial variation in inshore wave height from different perspectives. A representative set of wave conditions need to be pre-modelled with a close point spacing (around 50m) prior to analysis.

• Single Table Analysis

This analysis tool is most useful when trying to find the range of inshore wave heights predicted from a particular set of input parameters. It queries a single table of results. An inshore point separation is selected across which to calculate the wave height range. If the original data are for inshore points with a 50m spacing, then selecting a 5-point spacing will calculate a wave height range over each 200m section of coastline. If a point separation equal to the total number of points in the table is used, then the wave height range for the whole coastline is returned. Wave height ranges over a user-specified tolerance are highlighted in the results files as shown in Figure 3.17.

MaxPt, MinPt, maxHs, minHs, Range Hs(i), Tolerance flag

Point Separation = , 5

From I , to , 5 , 2.09 , 2.10 , 0.01

From 5 , to , 9 , 2.27 , 2.01 , 0.26 , TOLERANCE EXCEEDED From 9 , to, 13, 2.26 , 2.10 , 0 .1 6 , TOLERANCE EXCEEDED From 13 , to , 17 , 2.70 , 2.75 , 0.05

Figure 3.17 Output from analysis tool showing the range of inshore wave height calculated between sets of 5 inshore points (representing a distance of 200m) along the 6.5m contour to a tolerance of 0.1m.

In this example both 200m sections of coast between points 5 and 13 demonstrate more wave height variation than the 200m sections on either side.

• Multi-Table Range Analysis

This tool is most useful to investigate the changes in inshore wave height variation for a specific stretch of coast under different environmental conditions. It requires the same inshore point configuration to have been modelled under different wind strengths, durations or directions prior to analysis. By comparing the inshore wave height range for

the same inshore points from the different tables representing the different environmental conditions, it is easy to consider the response of a particular shoreline area.

• Optimum Point Space Tool

This tool calculates the minimum number of inshore points that should be used to model wave height variation within a certain tolerance along a coastline. It works one table at a time. A wave height range tolerance is specified against which to test all inshore point separations. For example, if the minimum number of inshore points necessary to model changes of wave height along the coast of 0.1m or more is required, then the wave height range tolerance specified is 0.1m. First a two point spacing of points is tested for all points to ascertain whether any two adjacent points display a wave height range over tolerance. If this is negative, then all three-point combinations are tested and so on. The process continues until a wave height range greater than 0.1m is identified. The point spacing is noted in the file and the calculation stops.

The purpose of all these tools is to analyse and summarise what could be large amounts of data describing the inshore wave climate.

3.5.2 Morphology Analysis Tools

The morphology analysis tools are a suite of mathematical functions that describe the shape of the input bathymetry. Global functions consider the overall shape of the bathymetry surface, and local functions investigate curvature within the surface to identify distinct concave and convex features. These tools have been developed in MATLAB scripts, with some use of Microsoft Excel spreadsheets for storing and displaying results. In terms of the overall structure of the IWMS (Figure 3.2), these tools are accessed through step 6. The global analyses use a one-way link as the output is in numerical form and the local analysis uses a two-way link to allow the data to be re­ imported into the GIS for visualisation and further analysis.

These tools can be used in two main ways: • Pre-modelling analysis

• Bathymetry change analysis

3.5.2.1 Pre-Modelling Analysis

One of the main problems when trying to model accurate inshore wave climates, is to determine which wave transformation model is the most appropriate for use for the bathymetry under consideration. Chapter 2 (section 2.3) discusses the main types of wave model available and outlines the wave processes that can be modelled successfully by each one. Unfortunately, there is no simple way to assess the requirement for a refraction- only or a réfraction-diffraction model for a particular bathymetry until the modelling has been completed and assessed against field validation measurements.

The morphology analysis tools in the IWMS can be used to address this issue. The morphological characteristics of any bathymetry can be assessed and quantified before modelling. If the accuracy of the SEAWORKS results are checked with field data, an assessment of the success of the modelling can be made. If the model is shown to produce greater than expected inshore wave heights, it is likely that this is due in part to the presence of wave diffraction. The bathymetry quantification can therefore be used to indicate the presence of surfaces likely to give rise to both wave refraction and diffraction.

Berkhoff et al. (1982), studied a single elliptical shoal and concluded that it was better modelled by combined refraction - diffraction models than refraction-only models. The morphology analysis tools available in the IWMS could be used to build on this study, to identify more features of particular shapes that can be labelled as ‘diffractive’. If this is not undertaken as part of a structured study, then ongoing archiving should be done to build up a database for modellers to consult before they model an area. This would allow better determination of the most appropriate model to use for any new study area.

S.5.2.2 Bathymetry Change Analysis

The morphology analysis tools can also be used to quantify temporal change in bathymetry areas. There are several examples of studies detailing attempts to quantify morphological change in long-term studies (Capobianco et al. (1999); de Vriend et al.

(1993); Sabatier et al. (2000)). These describe attempts to quantify the morphology of a wide range of coastal features. On a large scale there are studies into the changing shape of whole coastlines (de Vriend et al. 1993), down to much smaller scale features such as the sandbar known as the Epiguette Spit described by Sabatier et al. (2000).

Using bathymetry surveys of the same area taken at different times, the morphology analysis tools in the IWMS allow the changes in the bathymetry to be quantified. This can be done at large scale in terms of overall global change, and also at much smaller scales for the changes in size and position of particular convex or concave features on the seabed. The automation of these processes mean that for long-term studies where it is likely that personnel will change over the course of the study period, precision of results and the integrity of the comparisons can be maintained.

In the following sections, the theory behind the global and local analyses is discussed. Details of the algorithms and instructions explaining how to carry out the analyses are given in Appendix 4.

3.5.2.3 Global Analyses

The Global Analyses include the following: • Slope

• Aspect • Curvature

• Fractal Dimension

The determination of these parameters for an inshore area provides a mathematical description to be archived along with the bathymetry data. Automated procedures ensure

the repeatability of the calculations, which means that results for all bathymetry grids are comparable. This information will allow direct comparisons of global shape to be used

for a priori assessment of the complexity of a new area before modelling takes place.

• Slope and Aspect

The procedures for these calculations use a representative sample of ‘E, N, Depth’ grid positions which are automatically extracted from the bathymetry grid. Once extracted, the best-fit plane to this data sample is found using direction cosines and a least squares fitting routine. The procedures are implemented using Excel spreadsheet templates and MATLAB scripts.

• Global curvature

This uses quadratic fitting techniques and equations detailed in Wood (1996). The same mathematical theory underpins both the global and local curvature analyses although it is implemented in a different way in each case. In a similar way to the plane fitting, the best-fit quadratic is found for a sub-set of ‘E,N,Depth’ points. The overall convexity or concavity of the surface is described in the direction of steepest gradient, (longC or long curvature) and its orthogonal direction (crossC or cross curvature). The overall shape of the surface can be determined to describe whether the surface is elliptic, hyperbohc or parabolic. Procedures to implement the quadratic fit are again automated in MATLAB scripts.

• Fractal Dimension

Fractal Dimension (FD) is a measure of irregularity within a surface. Goodchild (1987) gives a good introduction to the theory behind the now well established method of calculating FD in surfaces using variogram analysis. This approach quantifies the elevation, or in this case, depth variation within a surface at a variety of scales, and generates the FD through the log/log relationship between these two variables. More details of the theory and methods applied in the techniques used in this research can be found in Burrough (1981), Burrough et a l (1998), Goodchild et a l (1987), Longley et a l

The context of coastal bathymetry is an unusual one for this type of analysis. The coastline itself was the original environment considered by Mandlebrot (1982) when the concept of the fractal dimension was first discussed. More recently, deep seafloor morphology was considered by Herzfeld et al (1999), in their study of the fractal nature of phenomena such as the Mid-Atlantic Ridge. The inshore area however, is one which in some ways could be expected to show low values of fractal dimension, and small variations in the parameter between different areas due to the smoothing action of the inshore waves which shape it. The IWMS provides tools to calculate the FD of the nearshore bathymetry. By encouraging their use, it is expected that patterns will emerge to demonstrate whether the fractal dimension of a seabed has a significant impact on the variation in inshore wave climates. It may also be able to be linked into investigations to ascertain whether wave diffraction is likely to be present in an area.

3.5.2.4 Local Analysis

The local analysis works by identifying the direction of curvature in small areas in order to identify areas of similar convexity or concavity in adjacent cells and hence isolate peaks or pits within the surface. MATLAB routines calculate the curvature of a subset of cells on the bathymetry surface. The calculation window, or ‘convolution filter’, assigns the curvature value to its central cell. The cell by cell approach ensures that the entire bathymetry surface is included in the calculations. Long and cross sectional curvature are calculated in the same way as with the global curvature analysis.

Once completed, the output from MATLAB is re-imported back into the GIS for further analysis and visualisation. Single features can be identified and re-combined with the original bathymetry data to isolate the bathymetry for that particular feature. An example of this is shown in Figure 3.18.

Bathymetry Values on Convex Feature

Figure 3.18 Local area analysis exaiuple sh ow in g the con vex features o f an offshore

bank

Once areas like this have been isolated another tool allow s the area, average depth and volum e o f the identified feature to be calculated. T hese three parameters can be used to quantify teiuporal change demonstrated by any particular seabed feature from sequential surveys in an area.

3.5.2.5 D iscu ssion

The m orphology analysis tools provide a package o f mathematical functions with which to quantify the shape o f the bathymetry. M ost o f them generate results that are directly applicable to the current m odelling. Som e h ow ever have been included to try to stimulate ‘good p ractice’ to a llo w an archive o f inform ation to be built up and linked with the results o f the subsequent w ave m odelling. This should eventually result in a database that should provide som e insight into particular bathymetry shapes and sizes that impact m ost on the ch oice o f the type o f w ave m odel to use in different circum stances.

In most cases com plete automation o f the functions ensures that the analyses w ill always be com parable regardless o f who actually carries them out. It should be noted that there are user defined elem en ts in the fractal dim ension calculation that m eans that results w ill

be non-standard unless these elements are specifically recorded and duplicated in each case.