1 Introduction Model description SBM-2DH Video Interpretation Model Model applications SBM-2DH...10

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4 Z336 November, 23 Contents 1 Introduction Model description SBM-2DH Approach Video Interpretation Model Bathymetry Assessment Model Running SBM-2DH Model applications SBM-2DH Synthetic test case RIPEX experiment California Combined nourishment Egmond Recommendations...2 Conclusions...21 i

5 Z336 November, 23 1 Introduction Beaches are very dynamic systems that change from day to day. One day, they may have long and straight bars, another they may have pronounced rip currents that are dangerous for swimmers. The safety of a dune system depends in part on the beach in front of it, so also here the state of the beach is important from a management point of view. Beach and nearshore nourishments are applied frequently; these adapt to the longer-term profile shape in a matter of weeks to months, through phases that may be unexpected and could pose temporary problems. Standard observation methods such as JARKUS in the Netherlands only give beach information at every 2 m, once per year, which is not enough to give information at this level of detail. Information of sufficient detail can be derived from high-resolution video observations of the nearshore zone. The ARGUS system directly gives an indication of the location of the main morphological features, but what we aim for here is the quantitative estimation of the by interpretation of the remotely sensed information. In this study, we present a method to derive two-dimensional (2D) sub-tidal beach from time-averaged video observations of wave breaking. Aarninkhof (23) presents a one-dimensional, time-dependent model to quantify subtidal beach along cross-shore arrays, on the basis of a time series of Argus video observations of wave energy breaking. The model named SBM ( Subtidal Beach Mapper ) determines surf zone through assimilation of video-observed and modelpredicted patterns of wave dissipation. Validation of SBM along two cross-shore Egmond transects approximately 14 m apart has shown that the model is able to reproduce the dominant morphological changes during the first year after a shoreface nourishment, including the shoreward migration of the outer bar and the net accretion of sand in the nearshore part of the surf zone. The rms error of the vertical deviations along the entire beach profile typically amounts to 4 cm for both arrays. Marginal deviations in the order of 1 to 2 cm are found at the seaward face of the bars, which increase up to 2 to 4 cm near the bar crest. Maximum deviations up to 8 cm are found in the trough region, owing to lack of wave dissipation information. The accretion of sediment in the shallow surf zone is underestimated. The Aarninkhof (23) version of SBM only operates along cross-shore arrays that are located within the field of view of a single video camera. For the Egmond station Jan van Speijk, this implies that SBM can only be applied to the far-field regions of cameras c1 and c and the central region of camera c3, as the cross-shore array has to extend from the seaward end of the surf zone (typically at 8 m offshore) to the shoreline. This limitation considerably hampers model applicability in support of coastal management. The goal of the present study is to extend the existing 1D model SBM for use in 2DH mode and to demonstrate the capability of the extended model, named SBM-2DH, at Egmond. To that end, this note discusses the background of SBM-2DH (particularly the quantitative interpretation of merged, plan view image intensities and the coupling of SBM-2DH and Delft3D), it presents three model applications (synthetic data, RIPEX experiment California 1

6 Z336 November, 23 and monitoring of a combined beach and shoreface nourishment Egmond), and it investigates the compatibility with high-resolution, video-derived data on intertidal beach. This study is a pilot project, which aims at demonstrating the capabilities of video imagery for the quantification of 2DH surf zone, rather than at developing a robust, automated and well-tested code. As a result, the present model involves a few manual steps (like the selection of good quality video data), which may be automated in future research projects. The work presented here has been performed as part of project 6 of the Voortschrijdend Onderzoeks Programma (VOP) of Rijkswaterstaat and, in the framework of assignment RKZ It received co-funding from the research project Data-Model-Integration and Delft University of Technology. The results also contribute to the outcome of the EU-funded CoastView project under contract number EVK3-CT

7 Z336 November, 23 2 Model description SBM-2DH This chapter discusses the background of SBM-2DH, with a focus on the modifications that were necessary to enable application of the SBM model in 2DH mode, particularly regarding the quantitative interpretation of plan view image intensities derived from multiple cameras, and the coupling of Argus video imagery and the Delft3D modelling system. 2.1 Approach The approach used by SBM-2DH to quantify subtidal from time-averaged image intensities is identical to that used by SBM (see Aarninkhof, 23). First, goodquality images with sufficient wave dissipation (i.e. considerable wave breaking occurring over at least one sand bar) are selected (hand-picked at this moment) and corrected for the effect of spatially and temporally varying background illuminations. Then the corrected image intensities are scaled such that they are a quantitative measure of wave dissipation. This video-derived dissipation pattern is compared to a dissipation pattern obtained from running an advanced 2DH wave transformation model across a recent nearshore which is either surveyed or determined from a previous image. Updating of the is achieved by raising the bottom elevation in areas where the video-measured dissipation exceeds the model-computed dissipation and vice versa. Since the SBM-2DH model can include video data with high resolution in time, it allows for nearly continuous monitoring of surf zone. Time series of video images Video image Argus metainformation Tide & wave information Initial Video Interpretation Model Measured wave dissipation Bathymetry Assessment Model Sub-model 1 Sub-model 2 Video-based Sub-tidal Beach Mapper (SBM-2DH) Evolution of sub-tidal Fig. 2.1: General layout of the 2DH Subtidal Beach Mapper SBM-2DH The general model layout (Fig. 2.1) reflects the operation of SBM-2DH. The Video Interpretation Model samples time-averaged video intensities, corrects them for the effect of background illuminations and variable contrast levels between cameras, and generates a video-based wave dissipation map. The Bathymetry Assessment Model adopts the latter to update surf zone through assimilation of video-derived and model-predicted 3

8 Z336 November, 23 measures of wave dissipation. Both sub-models will be discussed in the next two subsections. 2.2 Video Interpretation Model The Video Interpretation Model has to produce a wave dissipation map from a merged, plan view, time-exposure video image (obtained by an Argus station). A merged plan view image (Fig. 2.2), derived from multiple cameras, tends to show the outlines of the constituent images, because individual Argus images collected within a single hour come with variable intensity characteristics. Of particular interest are the overall image contrast and the crossimage variation (left to right) in overall intensity level. These differences are caused by different camera orientations with respect to the position of the sun and the nonsimultaneous collection of video data (SGI-based Argus-II video stations can only operate two cameras at the same time). Fig. 2.2: Merged, plan view image of Egmond station Jan van Speijk dd. 13/12/1999 at GMT 1: hr. The bright band at 7 m offshore shows the shoreface nourishment. A wave dissipation map for use with the Bathymetry Assessment Model is obtained from a merged plan view video image in a three-step approach. First we need to remove some background intensity level, because an area with no wave breaking should map to no wave dissipation, hence the video intensity should be set to zero there. To obtain smooth wave dissipation maps covering multiple cameras, we need to take into account differences in background intensity level between constituent images as well as the cross-image variation in overall intensity level in each constituent image. Secondly, we need to correct for variable image contrast levels between cameras, because an area represented by a low contrast image will result in unrealistically low wave dissipation levels in that area. Thirdly, we need to scale the corrected image intensities to a quantitatively correct measure of wave dissipation. Removal of spatially varying background intensity level Careful investigation of a series of Argus images has learned that oblique images often show a cross-image intensity trend. Merging multiple images then implies that the dark side of the first image is linked to the bright side of the second and so on, which yields clear transitions between cameras as shown in Fig To remove this cross-image intensity trend, a new method was developed which removes the background illumination level for each vertical image line U in the oblique image, i.e. prior to merging and plan view mapping. (NB: it is 4

9 Z336 November, 23 an image convention to denote image pixels in U,V space, (analogously to an x-y coordinate system), where vertical image lines are denoted U, horizontal image lines are denoted V, with the origin in the top left corner of the image). Starting at the left hand side of the image, the background illumination level µ dw is estimated for each vertical image line U, by taking the mean value of pixel intensities sampled along U between two shore-parallel lines outside the region of wave breaking. For the Egmond case the two shore-parallel lines were set at 8 m and 18 m offshore (Fig. 2.3a). To remove background illuminations along U, the mean intensity level µ dw observed at deep water is subtracted from all pixel intensities along U. Any negative pixel intensities thus obtained are set to zero. This approach yields smooth patterns of breaking-induced image intensities for each constituent image (i.e. per camera), but not necessarily for the full, merged plan view image. Fig. 2.3: Visualization of the deep water region used for the cross-camera removal of background illuminations (a) and the surf zone region used for the determination of image contrast (b). Correction for variable image contrast Breaking-induced image intensity maps resulting from the above correction procedure alone, showed that an individual low-contrast image (i.e. the contribution to the merged plan view from one particular camera) resulted in unrealistically low wave dissipation levels in that area. Thus, to obtain smooth wave dissipation maps covering multiple cameras, we need to correct for variable image contrast levels between cameras. This has been achieved by adopting the standard deviation σ sz of surf zone pixel intensities as an indicator for image contrast. An image-specific σ sz is determined from pixel intensities sampled from a nearshore region enclosed by shore-parallel lines at 1 m and 1 m offshore (Fig. 2.3b). Breaking-induced image intensities collected from different cameras are corrected according to I ci, σ sz,min = I i σ sz, i (Eq. 2.1) where σ sz,i is the standard deviation of surf zone pixel intensities of camera i, σ sz,min is the minimum σ sz of all cameras involved and I i is the breaking-induced image intensity map of camera i (after correction for background illuminations) and I c,i is the breaking-induced image intensity map after correction for variable image contrast. The ratio σ sz,min /σ sz,i

10 Z336 November, 23 typically varies between. and 1. Merging the corrected breaking-induced image intensity maps I c,i sampled from constituent cameras yields a more balanced wave dissipation map I v covering coastal stretches up to a few kilometers (Fig. 2.4). Fig. 2.4: Breaking-induced image intensity map I v of Egmond station Jan van Speijk after correction for variable camera contrast levels. The northward-oriented camera 1 (poiting towards the negative alongshore co-ordinate) shows improved contrast as compared to the original video data (Fig. 2.2). Scaling to a quantitatively correct measure of wave dissipation Following Aarninkhof (23), SBM-2DH relates breaking-induced image intensity patterns I v (Fig. 2.4) to hydrodynamic model-computed dissipation of roller energy (see Section 2.3). Consequently, the Video Interpretation Model normalizes I v such that Idxdy v = 1 and scales the normalized intensity map with the incoming wave energy flux to obtain a videoderived measure of wave dissipation D o that quantitatively matches the model-computed roller dissipation: x y Iv ( xy, ) Do( xy, ) = Ecg cos( θ ) dy y Idxdy v x y (Eq. 2.2) In Eq 2.2, E is the wave energy at the seaward end of the surf zone according to 1 2 E = ρ gh rms, c g is the wave group velocity and Θ is the wave angle of incidence with 8 respect to shorenormal. Wave conditions measured at deeper water are transformed to the seaward end of the surf zone with the help of a standard parametric wave model (Battjes and Janssen, 1978) including bottom friction, to account for the modification of wave height and direction due to wave refraction and bottom friction along the deeper part of the coastal profile. 6

11 Z336 November, 23 The present version of the Video Interpretation Model does not apply any correction to the image intensity signal to remove the effect of persistent foam drifting at the sea surface. This correction would involve the application of a 2DH version of the Breaker Intensity Model described by Aarninkhof (23), which is not operational yet. 2.3 Bathymetry Assessment Model The Bathymetry Assessment Model BAM updates the surf zone based on differences between the video derived D o (x,y) and the computed rate of roller dissipation D c (x,y) for the latest model (which may be either surveyed, or calculated by the model). For the updating process we use an analogy with morphodynamic modelling for fine sediment, as such an approach often leads to a sensible equilibrium. Instead of the bed shear stress that governs the stirring up or deposition of sediment, we introduce stirring and deposition terms based on the mismatch between D o (x,y) and D c (x,y). The sediment stirred or deposited leads to bottom changes, and these in turn lead to changes in D c (x,y); this decreases the mismatch, so that the model tends towards an equilibrium. BAM aims for a partial update of the bottom elevation on the basis of each single image, without achieving a perfect match between D o (x,y) and D c (x,y). A perfect match would imply an over-fit on noisy intensity characteristics of a particular image. The embedding of BAM in the framework of the overall time dependent model SBM-2DH enables the model to resolve the evolution of nearshore from a sequence of video images with clear wave dissipation patterns. z x z tide (t) Erosion h buf C s (x,y) z b (x,y) Accretion Fig. 2.: Definition sketch of the Bathymetry Assessment Model (BAM) BAM adopts a two-layer approach (Fig. 2.), consisting of the sea bed with elevation z b (x,y) and a sediment buffer layer with sediment availability C s (x,y), representing a sediment volume per unit area [m 3 /m 2 ]. A positive difference D c -D o drives an upward directed erosional sediment flux S E from the sea bed to the buffer layer, causing local erosion of the sea bed and a local increase of C s. A negative D c -D o results in the opposite effect, driving a downward direct accretional sediment flux S A which yields accretion of the sea bed and a decrease of C s in the buffer layer. Alongshore and cross-shore gradients of C s (x,y) drive the horizontal redistribution of sediment within the buffer layer from areas of large C s (sediment surplus in the buffer layer due to sea bed erosion) to regions of small C s (sediment shortage in the buffer layer due to sea bed accretion). The sediment buffer layer was included to guarantee conservation of mass within the overall system. Notice that C s is not related to any physical process like wave breaking induced turbulence. It only provides a sediment buffer to enable erosion and accretion of the sea bed, while facilitating the redistribution of 7

12 Z336 November, 23 sediments within the coastal system. Further details on the BAM model formulations can be found in Aarninkhof (23). Development of the Bathymetry Assessment Model in 2DH mode demands (1) the use of an advanced 2D wave transformation model to accurately describe the shoreward propagation of waves, (2) the inclusion of a 2D roller model to generate roller dissipation maps and (3) the incorporation of a 2D diffusion model to describe the redistribution of C s in the upper layer. The first demand requires the use of a directional wave model like SWAN (Ris, 1997) or HISWA (Holthuijsen et al., 1989), which determines the evolution of the wave direction from a spectral approach. Adopting a simpler approach by determining wave refraction from Snell s law would probably do well in the region of the outer bar, but certainly not in the inner surf zone. Solving the balance equations for wave and roller energy along these directions requires a 2D roller model (second demand) such as recently implemented in Delft3D-FLOW. The third demand requires the implementation of a robust, implicit 2D diffusion code, to solve for the distribution of C s in the upper layer. Considering these functional demands in relation to the functionality readily provided by the Delft3D modelling system, it was decided to implement BAM in the existing Delft3D environment rather than re-programming an advanced wave model plus diffusion code in a Matlab environment. Initial Observed dissipation map(s) Delft3D-FLOW Wave & Roller energy balance, flow field Computed dissipation ASSIM Source / sink terms sediment Advectiondiffusion model Update Delft3D-WAVE HISWA / SWAN Wave direction Fig. 2.6: Flow scheme BAM In Figure 2.6 the overall flow scheme of BAM is shown; the changes to the Delft3D code are limited to a routine ASSIM that reads the observed dissipation maps and interpolates them to the Delft3D grid (which may or may not be the same as the ARGUS grid) and to a section in the existing sediment source/sink routine. The implementation in Delft3D is such that the assimilation with ARGUS data can in principle be combined with a regular morphological run. Note that the embedding BAM in the operational Delft3D environment carries the additional benefit of providing improved opportunities for Data-Model Integration. Being highly 8

13 Z336 November, 23 complementary, there is a mutual benefit in integrating the two information sources. Video observations provide detailed information on the configuration of breaker bars, which can enhance the predictive capability of process-based morphological models through application of advanced data assimilation techniques and improvement of the process formulations in the model. Video observations, however, provide little information in areas outside the regions of actual wave breaking, which hampers the quantification of subtidal from video. These blank regions can be filled in with information on flow velocities or sediment transport rates, computed from morphological models. The key observation is that we are dealing with two complementary information sources, both with their own inaccuracies, whose combination provides added value in view of the development of a real-time coastal monitoring system. The embedding of BAM in the Delft3D environment is a crucial first step towards the realization of such an operational monitoring system. 2.4 Running SBM-2DH Setting up the Bathymetry Assessment Model in its present implementation comprises the following steps: 1. Create a model grid on the same domain as the ARGUS data; 2. Create an initial bathmetry, from data or using a uniform profile; 3. Set up a Delft3D-WAVE model using this grid as bottom grid, and a larger computational grid to avoid boundary disturbances; 4. Set up a Delft3D-FLOW model including sediment of type 'argus', using wave input from the WAVE model, and activating the roller model option;. Set up control module that alternately calls FLOW and WAVE. Water level and are passed from FLOW to WAVE, wave direction and period are passed from WAVE to FLOW. (The roller model computes wave energy, roller energy and dissipation) 6. Test that the model produces stable results on the given ; 7. Specify the number of images used, for how long each image is valid, filenames of dissipation images and their times. The model can be run for a single image or for a time-series of images. When available images are far apart in time, it is most convenient to split up the assimilation process into a number of separate runs, where the resulting for an image is passed on to the (short) simulation for the next image. Nonetheless, the model also allows for continuous simulations. 9

14 Z336 November, 23 3 Model applications SBM-2DH In the framework of this study, the behaviour and potential of SBM-2DH have been evaluated on the basis of three different test cases, namely (i) a synthetic data test (involving the use of artificially generated wave dissipation maps), (ii) the RIPEX experiment in Monterey, CA and (iii) a combined beach and shoreface nourishment in Egmond. The outcome of the three test cases is described in this chapter. 3.1 Synthetic test case In this section, we investigate the behaviour of SBM-2DH on the basis of a synthetic data set. Such a synthetic analysis involves the application of SBM-2DH on the basis of an artificial time series of beach bathymetries and hydrodynamic conditions. First, the Delft3D wave model is used to compute a data set of roller dissipation patterns, based on the known and hydrodynamic conditions. Next, the computed dissipation patterns are interpreted as the measured wave dissipation D o (i.e., as if they were measured from video). Starting from an arbitrary initial (for instance a uniform beach) and the artificially generated set of D o, the Bathymetry Assessment Model of SBM-2DH can be used to re-create the original beach from an ideal data set of wave dissipation patterns plus the actual hydrodynamic conditions. Such a synthetic analysis enables us to verify correct implementation of the BAM model formulations and to gain insight in BAM behaviour for different model parameter settings. Fig. 3.1: Initial for the synthetic test case (backward run) and the superimposed Gaussian hump to be re-constructed. 1

15 Z336 November, 23 The synthetic test case considered here involves the modelling of a shoreface nourishment, superimposed on a uniform with a 1:1 beach slope and a 2 m water depth at the seaward boundary (Fig. 3.1). The nourishment is described as a Gaussian-shaped hump of 3 m height and with a standard deviation of 3 m, located at m water depth in the central part of the model. The computational domain measures 2 m cross-shore by 2 m alongshore, with a relatively course grid spacing of 4 m and 1 m, respectively. The model is forced by waves with a constant wave height H sig = 2 m, peak period T peak = 7 s and wave angle Θ = 3 deg. with respect to shorenormal. During the experiment, the water level is linearly lowered from z = m to z = -2.4 m. The latter is done to obtain a wave dissipation signal along a larger portion of the beach profile, as wave dissipation patterns migrate seaward with a decreasing water levels (e.g. Van Enckevort and Ruessink, 21). The forward simulation to generate synthetic roller dissipation maps is run over a nourished beach topography with a time step of.1 minute. The total simulation period is 2 hours. To initialize the model, the water level is set a constant level z = m throughout the first hour. During the second hour, the water level is linearly lowered from z = m to z = -2.4 m and wave dissipation maps are written to file every 1 minutes. This yields a data set of 7 wave dissipation maps, which provide the input for the backward simulation. The backward simulation is also run with a time step of.1 minute, but starts from a uniform beach (Fig. 3.1, left panel). Like the forward run, the backward simulation covers a total simulation period of 2 hours, the first hour again being used for model initialization. After 1 hour, SBM-2DH picks up the first artificially generated wave dissipation map and starts re-creating the nourished beach. Every 1 minutes, the measured dissipation pattern is updated on the basis of the dissipation maps that were written to file during the forward simulation. The final result after 2 hours is shown in Fig. 3.2 by means of the measured roller dissipation (or target dissipation, left panel), the final (middle panel) and the cumulative bottom changes (right panel). Notice that the color bar of the plot ranges from -8 m to m in stead of -2 m to m, to highlight the contours the shoreface nourishment. 11

16 Z336 November, 23 D r (N/m/s) z b (m) z b (m) Fig. 3.2: Reconstruction of a Gaussian-shaped shoreface nourishment from synthetic roller dissipation maps. Final result after 2 hours. The right panel of Fig. 3.2 shows that SBM-2DH successfully reconstructs a shoreface nourishment of approximately 3 m height. Bathymetrical changes are virtually zero outside the region of the nourishment. The evolution of bathymetrical changes over time is presented in Appendix A. The models initially predicts erosion of sediment at the lee side of the nourishment, which can be attributed to the initial absence of the nourishment in the backward simulation which induces an overestimate of roller dissipation rates near the shoreline (hence erosion). With ongoing accretion in the nourished area, the nourishment increasingly attracks wave dissipation, which causes a decrease of roller dissipation rates near the shoreline. The latter induces the accretion of sediment, thus revealing a favorable self-correcting mechanism. Although it would be appropriate to extend the synthetic analysis to explore model behaviour over a wider range beach configurations, hydrodynamic conditions and parameter settings, we presently conclude that the synthetic test case considered here suggests that the model formulations are correctly implemented and that SBM-2DH allows for morphodynamic computations at realistic spatial scales. 3.2 RIPEX experiment California A preliminary first field test case was carried out using data from the RIP current Experiment (RIPEX), Reniers et al., 22). This experiment took place in Monterey Bay in the spring of 21. Available data used here are offshore wave parameters, measured at May, 21 and one observed video image on May 6, 21 at 21: hrs, provided by Nathaniel Plant at NRL in Stennis. Waves measured at deep water had wave height Hrms =.7 m, Tp = 1.7 s, direction almost completely shore-normal. 12

17 Z336 November, 23 The video image consisted of one camera view only; the original image is shown in Figure 3.3. This image was extended in alongshore direction in order to obtain a rectangular area; the dry part was set to. Image intensities were corrected for background intensity levels and scaled to quantitatively match computed roller dissipation rates with the help of the methodology introduced in Section 2.2. Fig. 3.3: Video image RIPEX experiment The measured profile at Y= m was used to produce an initial that was uniform in alongshore direction. The computational grid has a grid size approx. 2.3 m by m, which is 1x1 times as coarse as the (very high-resolution) video data. The flow+roller model was run with a time step of 1.2 s; the wave directions were updated every 3 flow time steps by running HISWA over a larger domain. The result after spinning up the roller model on the initial is shown in Figure 3.4. The lower two panels show the dissipation measured from video (or target dissipation ) and the measured, the top panels the initial state of the model. The measured shows very well developed rip channels, which are clearly reflected in the video image. Morphological updating started after minutes of initialisation time, mainly needed to spin up the roller model. Figure 3. shows the updated and dissipation map after another 7 minutes (21 steps, 7 HISWA updates). The pattern of dissipation matches the target pattern very well, except in the very nearhore. The adjusted shows rip channels at roughly the same locations as in the measured. 13

18 Z336 November, 23 In Figure 3.6 shows some cross-sections at m spacing, roughly coinciding with rip channels and bar crests. The area seaward of X=1 m, with a depth between 1 and 4 m, shows a remarkably good match, with deviations in the order of dm s. In the very shallow area the agreement is not good. Results for an alongshore transect are shown in Figure 3.7. The locations of bar crests and troughs in the middle part agree within 1-2 m, and the depths at the shallowest parts within a couple of dm s; discrepancies in the rip channel depths are larger as could be expected. Despite the large deviations at shallow water depths of less than 1 m, we conclude that the results are promising, particularly given the fact that only one image was used up till now. Fig. 3.4: Initial state of model, RIPEX experiment Monterey Bay 14

19 Z336 November, 23 Fig. 3.: Final state of model after 12 minutes of simulation time Fig. 3.6: Comparison of initial, computed and observed cross-shore profiles 1

20 Z336 November, 23 Fig. 3.7: Comparison of computed and observed longshore profiles of depth (left) and intensity (right) 3.3 Combined nourishment Egmond To assess the capability of SBM-2DH for a Dutch coastal site, the model is applied to quantify the evolution of subtidal along a 2 km coastal stretch at Egmond, over a 18 month period starting December During this period, the WESP was used to survey nearshore twice per year, typically along cross-shore profiles with 1 m spacing alongshore. The measured depth is estimated to have an error of less than 1 cm. The survey data were interpolated to a rectangular grid with a cross-shore and alongshore spacing of 1 m, yielding good-quality bathymetrical maps of September 1999, May 2, September 2 and April 21. Offshore wave conditions (H rms, T p and Θ) were measured with a directional wave buoy at IJmuiden, located approximately 1 km to the south of the nourished site. Approximately % of the missing data, which occurred during 1% of time, could be replaced by values from an identical buoy approximately 7 km to the north. Offshore tidal levels are found from interpolation in water level data collected at tidal stations located 1 km north and south of Egmond. Video data were collected hourly with an Argus video station, installed at 43 m above sea level on top of the Egmond lighthouse Jan van Speyk for the purpose of monitoring the morphological evolution of the nourished area. For the purpose of this study, time-averaged video intensity data were sampled from all five cameras, covering the central part of the 16

21 Z336 November, 23 nourished area up to 1 km north and south of the video station. SBM-2DH computations are performed on the basis of 13 hand-picked, merged images of good-quality and sufficient wave breaking, which are indicated in Fig Fig. 3.8: Characterization of the Egmond data set. Evolution of wave height during the study period (solid line) and times of good-quality video images (circles) The results of the SBM-2DH application at Egmond are shown in Appendix B. The computed bathymetries at 17//2, 28/9/2 and 19/3/21 are compared to the surveyed batyhmetries of May 2, September 2 and April 21 (Fig. B.2a B.2c). Figures B.3a B.3c present the vertical model deviations in the months listed above (right panel), as well as the morphological changes measured in the field between September 1999 and the month of interest. Including the latter provides the opportunity to assess the added value of SBM-2DH as compared to the situation where you would assume no morphological changes at all (i.e. the situation where the most recent bathymetrical survey is the best estimate of present ). To facilitate the interpretation of results, Figures B.4a B.4g provide the morphological evolution along 7 cross-shore beach profiles spacing 3 m alongshore, located within 9 m on each side of the video station. Finally, as a measure of overall model accuracy, Fig. B. shows the cross-shore variation of the root mean square vertical error determined along shore-parallel lines covering the entire grid in alongshore sense. In the remainder of this section, we present a preliminary analysis of SBM-2DH behaviour based on a very concise analysis of the data available. Along the upper part of the beach profile (i.e. landward of the subtidal bars), the results show a continuous lowering of the bed elevation (Fig. B4.a-B4.g). This is induced by the type of landward boundary condition presently applied, which may be reconsidered for future applications. In the central part of the study area, fair results are found in the inner surf zone during the first few months, showing some underestimation of water depth. This turns into an overestimation of water depth during the second part of the study period. More detailed investigation of the results learn that this increase in water depth is initiated by the inclusion of video data sampled at July 12, 2, which apparently cannot be corrected afterwards. This observation confirms the potentially negative effect of including noisy video data, in combination with relatively large bathymetrical updates per image. An overestimate of the water depth at shallow water is also observed in the northern and southern part of the model, indicating that video-derived roller dissipation patterns underestimate the actual dissipation rates. This may be induced by relatively poor image contrast for the far-field regions in the outer cameras. 17

22 Z336 November, 23 At deep water (i.e. seaward of the breaker bars), the model results show a continuous overestimate of the water depth from the first image onwards, which increases with time. This suggests the accumulation of small errors over time, caused by an underestimate of video-derived dissipation rates. To improve model performance at this point, we need to reconsider the procedure for the correction of background illuminations, which presently sets wave dissipation rates to zero at deeper water. As a result, any model-predicted wave dissipation in the region will induce a lowering of the bed elevation. Besides, calibration of the wave model (for instance, an increase of the dissipation parameter γ) will also have a positive effect. In the bar zone, the application of SBM-2DH yields moderately good results. The outer bar and trough region initially show fair results, which suddenly turns into a large overestimation of the trough depth. The background of this non-favorable shift needs further investigation. The middle bar and trough region generally show an overestimation of the water depth at both the bar crest and the trough, albeit that the relative difference between both (or bar height ) is remarkably correct. Owing to the considerable morphodynamic activity in the inner surf zone at the time scale of days to weeks, an absolute interpretation of model accuracy in this shallow region is hampered by the time offset between the dates of the field survey and the model computation. Rip current (?) Fig. 3.9: Plan-view Argus video image and SBM-2DH computed flow field at Egmond dd. June 22, 2. The Argus image shows the central part of the nourished area, covering cross-shore and alongshore distances of (approximately) 3 m and 6 m. The coordinates along the computed flow field are referenced against the RD-coordinate system, which has its origin in Paris and a positive x (y) axis pointing towards the east (north). The model deviations qualitatively reported above are confirmed in Fig. B., which shows that rms profile deviations are initially in the order of tens of centimeters. Profile deviations are found to increase over time, which suggests the accumulation of small errors. Unrealistically large rms deviations are found in the trough regions and near the shoreline, 18

23 Z336 November, 23 both regions that need further consideration. However, it should be noted that no attention has been paid so-far to the calibration of SBM-2DH (nor the wave model, nor the Bathymetry Assessment Model). Furthermore, the model manages to preserve the characteristic bar-trough configuration throughout the entire 1. year study. Promising model performance is further revealed from the realistic flow field computed in June 2 (Fig. 3.9), showing a strong rip current crossing the inner bar. The corresponding plan-view video image of June 22 vagely reveals a depression in the wave dissipation pattern at the same location, which may also reflect the presence of a rip current. The latter observation clearly highlights the potential of the combination of sophisiticated models and highresolution video. 19

24 Z336 November, 23 4 Recommendations On the basis of the concise analysis of SBM-2DH behaviour performed so-far, we recommended the following research activities to improve model performance: Inclusion of video-derived intertidal beach bathymetries as a shoreward boundary condition of SBM-2DH. As the performance of wave models generally decreases at shallow water owing to the non-linearity of the wave field, the inclusion of highlyaccurate IBM results would certainly contribute to SBM-2DH performance in this area. The potential for this extension is illustrated with the help of Figures B.4a - B.4g, which show estimates near the shoreline obtained from Caljouw (2) and Nipius (22), generated with the Intertidal Beach Mapper (Aarninkhof, 23). Improvement of the Video Interpretation Model. Although the new approach for the quantitative interpretation of 2DH merged images yields surprisingly smooth wave dissipation maps covering multiple cameras, video-derived dissipation maps still appear to underestimate wave dissipation rates in the far-field of the outer cameras. Furthermore, no correction for the effect of persistent foam drifting at the water surface (cf. Aarninkhof, 23) has been applied yet. As the inclusion of poor quality video data was found to strongly affect the performance of SBM-2DH, further testing and improvement of the Video Interpretation is of crucial importance. Incorporation of large numbers of images. The present Egmond application was based on a limited number of video images, hence relatively large morphological updates per image. As a result, individual, poor-quality images strongly affect the model outcome. To reduce the negative contribution of individual, poor quality images, it is necessary to incorporate many more video images to arrive at a gradual update of surf zone. Improvement and calibration of the Bathymetry Assessment Model. So-far, no attention has been paid to the calibration of the erosion and sedimentation parameters of the Bathymetry Assessment model. Earlier work (Aarninkhof, 23) has shown that these parameter strongly govern model performance. In addition, we could consider the development of a threshold criterion on wave dissipation levels, to avoid the accumulation of small errors over time due to the inclusion of noisy video data. Calibration of the Delft3D wave model. Proper calibration of the wave model (particularly the wave dissipation parameter γ) may yield improved estimates of the roller dissipation, that better match the video-derived dissipation rates. Besides, the use of an alternative wave dissipation formulation (e.g. Baldock, 1998) may positively affect SBM-2DH performance. Inclusion of model-derived process information. The inclusion of model-derived process information, like flow velocities or sediment transport rates, allows for the improvement of SBM-2DH performance in areas without any wave dissipation (like the trough regions and at deeper water). This extension demands the use of Delft3D in fully mophological mode. 2

25 Z336 November, 23 Conclusions The goal of this pilot study was to extend an existing 1D model for the quantification of subtidal water depth from video observations of wave breaking for use in 2DH mode, and to demonstrate its capabilities on the basis of three different test cases. With respect to model development and implementation, the major findings of this study are: The newly developed Video Interpretation Model yields surprisingly smooth wave dissipation maps covering multiple cameras, which however still appear to underestimate wave dissipation rates in the far-field of the outer cameras (causing an underestimate of the bottom elevation in those regions). The methodology for the update of subtidal beach through assimilation of video-derived and model-predicted patterns of wave dissipation has successfully been integrated in the Delft3D modelling environment, so that it benefits from existing functionality offered by the operational Delft3D model. The practical potential of the newly developed model named SBM-2DH has been investigated through application to three different test cases. The main findings of this application phase are: For a synthetic test case (involving the use of artificially generated wave dissipation patterns), the model successfully re-creates a shoreface nourishment. Although it would be appropriate to extend the synthetic analysis to explore model behaviour over a wider range beach configurations, hydrodynamic conditions and parameter settings, the single test considered here suggests that the model formulations are correctly implemented and that SBM-2DH allows for morphodynamic computations at realistic spatial scales At Monterey Bay (California), the model realistically reveals a rip current system from a single video image. With vertical deviations in the order of tens of centimeters, model results are remarkably good at water depths in exceedance of 1 m. In more shallow areas, the agreement is not good. At Egmond, SBM-2DH bed level deviations initially are in the order of tens of centimeters and increase over time. Except for the central part of the nourished area during the first part of the study period, the model shows a persistent overestimation of the water depth. Model performance in the bar regions is relatively good as compared to the neighbouring trough regions. Still, the model manages to preserve the bar-trough geometry, which is an important and favorable model characteristic. Overall, the model was found to robustly converge within tens of seconds, which makes our approach towards the quantification of two-dimensional entirely feasible. The integration of high-resolution video observations and the sophisticated Delft3D modelling environment as achieved in the framework of this study opens the door towards the development of an operational monitoring system for the real-time prediction of hydrodynamic processes and morphological changes in the nearshore zone 21

26 Z336 November, 23 References Aarninkhof, S.G.J. (23). Nearshore Bathymetry derived from video imagery. PhD. thesis, Delft University of Technology, 17 pp. Baldock, T.E., Holmes, P. Bunker, S. and Van Weert, P. (1998). Cross-shore hydrodynamics within an unsaturated surf zone. Coastal Engineering 34, pp Caljouw, M. (2). Video-based monitoring of beach- and shoreface nourishments. MSc. thesis, Delft University of Technology. MacMahan, J., Reniers, A.J.H.M., Thornton, E.B., Stanton, T. and Dean, R.G. (22). RIPEX: Rip current pulsation measurements. Proc. of the 28 th International Conference on Coastal Engineering, Cardiff (Wales), pp Nipius, L. (22). Evaluation of nourishments at Egmond with Argus video monitoring and Delft3D-MOR. MSc. thesis, Delft University of Technology. Reniers, A.J.H.M., MacMahan, J., Thornton, E.B. and Stanton, T. (22). RIPEX: Ripcurrent pulsation modelling. Proc. of the 28 th International Conference on Coastal Engineering, Cardiff (Wales), pp Ris, R.C. (1997). Spectral modelling of wind waves in coastal areas. PhD. thesis, Delft University of Technology, 162 pp. Van Enckevort, I.M.J. and Ruessink, B.G. (21). Effects of hydrodynamics and on video estimates of nearshore sandbar position. Journal of Geophysical Research, 16, pp

27 Z336 November, 23 Appendix A 23

28 Time t = 61 mn. Time t = 81 mn Time t = 11 mn. Time t = 121 mn SBM 2DH Application synthetic data WL DELFT HYDRAULICS SBM 2DH Z336 Fig. A.1

29 Z336 November, 23 Appendix B 24

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37 Bottom elevation [m] 1 Measured May 2 Measured Sep Computed SBM 2DH May 2 IBM May x 1 Bottom elevation [m] 1 Measured Sep. 2 Measured Sep Computed SBM 2DH Sep. 2 IBM Sep x 1 Bottom elevation [m] 1 1 Measured Apr. 21 Measured Sep Computed SBM 2DH Apr. 21 IBM Apr x 1 Profile evolution transect Y= 9m (approx. RD Y=167) Egmond station Jan van Speijk WL DELFT HYDRAULICS Z336 SBM 2DH Fig. B.4a

38 Bottom elevation [m] 1 Measured May 2 Measured Sep Computed SBM 2DH May 2 IBM May x 1 Bottom elevation [m] 1 Measured Sep. 2 Measured Sep Computed SBM 2DH Sep. 2 IBM Sep x 1 Bottom elevation [m] 1 1 Measured Apr. 21 Measured Sep Computed SBM 2DH Apr. 21 IBM Apr x 1 Profile evolution transect Y= 6m (approx. RD Y=138) Egmond station Jan van Speijk WL DELFT HYDRAULICS Z336 SBM 2DH Fig. B.4b

39 Bottom elevation [m] 1 Measured May 2 Measured Sep Computed SBM 2DH May 2 IBM May x 1 Bottom elevation [m] 1 Measured Sep. 2 Measured Sep Computed SBM 2DH Sep. 2 IBM Sep x 1 Bottom elevation [m] 1 1 Measured Apr. 21 Measured Sep Computed SBM 2DH Apr. 21 IBM Apr x 1 Profile evolution transect Y= 3m (approx. RD Y=18) Egmond station Jan van Speijk WL DELFT HYDRAULICS Z336 SBM 2DH Fig. B.4c

40 Bottom elevation [m] 1 Measured May 2 Measured Sep Computed SBM 2DH May 2 IBM May x 1 Bottom elevation [m] 1 Measured Sep. 2 Measured Sep Computed SBM 2DH Sep. 2 IBM Sep x 1 Bottom elevation [m] 1 1 Measured Apr. 21 Measured Sep Computed SBM 2DH Apr. 21 IBM Apr x 1 Profile evolution transect Y= m (approx. RD Y=1478) Egmond station Jan van Speijk WL DELFT HYDRAULICS Z336 SBM 2DH Fig. B.4d

41 Bottom elevation [m] 1 Measured May 2 Measured Sep Computed SBM 2DH May 2 IBM May x 1 Bottom elevation [m] 1 Measured Sep. 2 Measured Sep Computed SBM 2DH Sep. 2 IBM Sep x 1 Bottom elevation [m] 1 1 Measured Apr. 21 Measured Sep Computed SBM 2DH Apr. 21 IBM Apr x 1 Profile evolution transect Y= 3m (approx. RD Y=1448) Egmond station Jan van Speijk WL DELFT HYDRAULICS Z336 SBM 2DH Fig. B.4e

42 Bottom elevation [m] 1 Measured May 2 Measured Sep Computed SBM 2DH May 2 IBM May x 1 Bottom elevation [m] 1 Measured Sep. 2 Measured Sep Computed SBM 2DH Sep. 2 IBM Sep x 1 Bottom elevation [m] 1 1 Measured Apr. 21 Measured Sep Computed SBM 2DH Apr. 21 IBM Apr x 1 Profile evolution transect Y= 6m (approx. RD Y=1419) Egmond station Jan van Speijk SBM 2DH WL DELFT HYDRAULICS Z336 Fig. B.4f

43 Bottom elevation [m] 1 Measured May 2 Measured Sep Computed SBM 2DH May 2 IBM May x 1 Bottom elevation [m] 1 1 Measured Sep. 2 Measured Sep Computed SBM 2DH Sep. 2 IBM Sep x 1 Bottom elevation [m] 1 1 Measured Apr. 21 Measured Sep Computed SBM 2DH Apr. 21 IBM Apr x 1 Profile evolution transect Y= 9m (approx. RD Y=1389) Egmond station Jan van Speijk WL DELFT HYDRAULICS Z336 SBM 2DH Fig. B.4g

44 RMS error May 2 4 RMS error [m] x 1 RMS error Sep. 2 4 RMS error [m] x 1 RMS error Apr RMS error [m] x 1 Cross shore evolution of RMS error in bed elevations Egmond station Jan van Speijk WL DELFT HYDRAULICS SBM 2DH Z336 Fig. B.

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