# Morphodynamic Acceleration Techniques for Multi-Timescale Predictions of Complex Sandy Interventions

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## Abstract

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## 1. Introduction

## 2. Morphodynamic Acceleration Techniques

#### 2.1. Techniques Using Brute Force Time Series

#### 2.1.1. Existing Brute Force Techniques

#### 2.1.2. New Technique: Brute Force Merged

#### 2.2. Techniques Using Representative Wave Conditions

#### 2.2.1. Input Reduction Based on Longshore Sediment Transports

#### 2.2.2. Input Reduction Based on Offshore Wave Climate

#### 2.3. Strengths and Limitations of the Morphodynamic Acceleration Techniques Considered

## 3. Case Study: The Sand Engine

#### 3.1. Case Description

#### 3.2. Numerical Model Setup for Delfland Coast

#### 3.3. Validation of the Benchmark Simulation without Upscaling (Brute Force)

#### 3.3.1. Morphodynamic Evolution

#### 3.3.2. Volume Changes 2011–2016

## 4. Application of Acceleration Techniques to the Sand Engine Case

#### 4.1. Methodology for Brute Force Methods

#### 4.2. Methodology for Representative Wave Forcing Techniques

#### 4.2.1. Techniques Based on longshore transports

^{2}, and standard deviation). In this way, an optimal set of wave conditions can be determined based on statistical parameters. Typically, a set of 10–15 wave conditions can represent the LST distribution quite well. Here, OPTI was applied for the alongshore distribution of both the net and gross transports.

#### 4.2.2. Technique Based on Offshore Wave Climate

## 5. Verification of Acceleration Techniques for Short to Medium Term Morphodynamic Evolution

#### 5.1. Morphological Response

#### 5.2. Volume Changes 2011–2016

#### 5.3. Computational Times

## 6. Comparison Acceleration Techniques for Decadal Forecasts

#### 6.1. Decadal Scale Evolution

#### 6.2. Volume Changes 2011–2040

#### 6.3. Shoreline Positions in 2040

## 7. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Schematic diagram of the spatio-temporal impacts of two coastal interventions; a large-scale nourishment (

**left**) and a port (

**right**). The bottom panels show the relative importance of processes across the different time scales.

**Figure 2.**Graphical overview of (

**a**) the non-scaled brute force approach (BF) and the three considered morphodynamic acceleration techniques using brute-force time series: (

**b**) brute force filtered (BFF), (

**c**) brute force-filtered and compressed (BFFC), and (

**e**) brute force merged technique (BFM). (

**d**) shows the basic morphodynamic feedback loop used in BF, BFF, and BFFC. The red wave time series in (

**a**–

**c**) indicate the selected wave time series.

**Figure 4.**Graphical overview of the three considered morphodynamic acceleration techniques using representative (schematized) conditions: (

**a**) NLST, (

**b**) GLST and (

**c**) OWC.

**Figure 5.**Geographical setting of the Delfland coast and the location of the Sand Motor (

**left**). Aerial photographs from the Sand Motor in 2011 and 2016 (

**right**).

**Figure 6.**Overview of observed and computed bed levels and changes for 2016. (

**a**) presents the observed bed levels in August 2011, (

**b**) shows the observed bed levels in September 2016, (

**c**) presents the computed bed levels in September 2016, while the observed and computed bed changes are presented in (

**e**) and (

**f**), resp.

**Figure 7.**Computed and observed behavior of volume changes in the three control areas for the period August 2011–August 2016. Red dots and line represent the volume change in the peninsula area, while the blue (green) dots and lines show the volume changes in the northern (southern) section. The grey dots and lines show the sum of the volume changes in all three control areas.

**Figure 8.**Envelope of the alongshore distribution of the annual (

**a**) net and (

**b**) gross longshore transports for the years 1985–2015. The grey lines are the annual longshore transport rates calculated using the 214 wave conditions and the measured probability of occurrence of the waves for each year. The black curves indicate the (

**a**) net and (

**b**) northerly and southerly longshore transports based on 10 wave conditions derived with OPTI to be used in the NLST and GLST computations.

**Figure 9.**Wave rose and density distribution of wave height vs. direction for Europlatform (left) and IJmuiden (right). The black line at 311 ${}^{\xb0}$N represents the angle of the shore normal at the ZM location prior to implementation.

**Figure 10.**Resulting wave conditions for the three considered wave input reduction techniques. The black dashed line represents the angle of the shore normal at the ZM location prior to implementation, while the green dotted lines indicate the selected H${}_{s}$-direction bins for the OWC technique.

**Figure 11.**Observed and computed bed levels after one year: (

**a**) observed bathymetry in August 2011, (

**b**) observed bathymetry in August 2012, and the predicted bathymetries in August 2012 for (

**c**) NLST, (

**d**) GLST, (

**e**) OWC, (

**g**) BF, (

**h**) BFF, (

**i**) BFFC, (

**j**) BFM. Cross-shore profiles for all observations and simulations are presented in (

**f**) along the red transect shown in (

**g**). The red line represents the $-4$ m MSL depth contour, while the thick black line shows the 0 m MSL depth contour.

**Figure 12.**The upper panel presents the volume change in the period 2011–2016 for the three ZM control areas (see Figure 7) for all considered acceleration techniques. The bottom panel presents the RMSEin each year for the four different acceleration techniques and the BF with respect to the observed erosion of the peninsula control areas. Due to long computational times the results of the BFF simulation are presented only for the first two years.

**Figure 13.**Computed bed levels in 2040 for the four considered acceleration techniques: (

**a**) BFM, (

**b**) NLST, (

**c**) GLST, and (

**d**) OWC. The white dotted line indicates the 2011 coastline just after ZM construction.

**Figure 14.**Volume changes in the period 2011–2040 for the four considered acceleration techniques for the three control areas (see Figure 7).

**Figure 15.**Predicted shoreline positions in 2040 for the four considered acceleration techniques showing the interscale impacts.

**Table 1.**Computational times for all considered acceleration techniques; computational times presented as hours run time to compute bed level changes after one year.

Abbr. | Acceleration Technique | Time (h) | Relative to BF (%) |
---|---|---|---|

BF | Brute Force | 331.4 | 100.0 |

BFF | Brute Force Filtered | 176.6 | 53.3 |

BFFC | Brute Force Filtered Compressed | 56.5 | 17.1 |

BFM | Brute Force Merged | 15.0 | 4.5 |

NLST | Net LST | 3.9 | 1.2 |

GLST | Gross LST | 3.2 | 1.0 |

OWC | Offshore Wave Climate | 5.3 | 1.6 |

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**MDPI and ACS Style**

Luijendijk, A.P.; de Schipper, M.A.; Ranasinghe, R. Morphodynamic Acceleration Techniques for Multi-Timescale Predictions of Complex Sandy Interventions. *J. Mar. Sci. Eng.* **2019**, *7*, 78.
https://doi.org/10.3390/jmse7030078

**AMA Style**

Luijendijk AP, de Schipper MA, Ranasinghe R. Morphodynamic Acceleration Techniques for Multi-Timescale Predictions of Complex Sandy Interventions. *Journal of Marine Science and Engineering*. 2019; 7(3):78.
https://doi.org/10.3390/jmse7030078

**Chicago/Turabian Style**

Luijendijk, Arjen P., Matthieu A. de Schipper, and Roshanka Ranasinghe. 2019. "Morphodynamic Acceleration Techniques for Multi-Timescale Predictions of Complex Sandy Interventions" *Journal of Marine Science and Engineering* 7, no. 3: 78.
https://doi.org/10.3390/jmse7030078