Coasts are constantly under the pressure of hydrodynamic conditions such as waves, tides, and storms. In the Netherlands, sand nourishments are executed every few years in order to maintain the country’s sandy beaches for purposes of safety, recreation, and ecology. In order to determine where to carry out these nourishments, the whole sandy coastline of the Netherlands is measured annually by Rijkswaterstaat. This annual survey, called JAarlijkse KUStmetingen (JARKUS), monitors where (increasing) erosive trends appear or persist and, due to its timespan (dating back to 1843), is a valuable dataset to understand the evolution of the Dutch coast. However, this measurement survey is restricted to its annual frequency and is costly. The use of optical satellite imagery for measuring land cover types and geographic features is rapidly becoming more popular due to their high temporal frequency and relatively low costs (due to the public availability of some satellite missions, such as NASA’s Landsat and ESA’s Sentinel-2 missions). This research studied the possibilities of using optical satellite imagery for measuring beach width dynamics in addition to the existing measurement campaigns.

In this study, we derived the Satellite-Derived Beach Width (SDBW) as the cross-shore distance between the Satellite-Derived Shoreline (SDS) and the Satellite-Derived Vegetation line (SDV). We adopted a widely used and validated method for SDS detection and adapted this method to establish the SDV detection method. The SDS and SDV are derived from optical satellites by deriving vectors from the border between two contrasting land cover types that are identified by differences in (sun)light reflectance values. The SDS and is derived from the contrast between water and land, the SDV from the contrast between and sand/sediment and vegetation. The SDBW data was measured from both composite and individual satellite images. The different techniques are suited for different applications, and both have their advantages and disadvantages. A composite image is an image that is composed of a sequence of individual satellite images available within a set window. E.g., a composite is the average image of all those images. Recent research showed that, at the cost of temporal resolution, composite images are suited for analysis of long-term (structural) shoreline trends since they mitigate certain factors influencing image quality (such as clouds and cloud shadows, waves and tides, and satellite instrument errors). Individual images are better suited for analyses of short-term dynamics since they provide instantaneous measurement data. However, they are hampered more by the factors mentioned above, and hence need to be screened before use...

The world's coasts are at risk: an estimated 24% of the world's beaches are eroding. Extreme events such as storms continuously threaten the coastal region and pose a serious risk of coastal flooding. Under the influence of climate change, the seaward pressures are only expected to increase. Sea level rise is accelerating, storm intensities and frequencies will increase, and precipitation will become more extreme. At the same time human pressures such as coastal tourism and rapid urbanisation impose stresses to the coastal system. An estimated 23% of the world's population lives in the coastal zone where natural hazards cause a direct flood risk. Protection of the coastal region becomes more and more relevant. Coastal management strategies need to take the local circumstances into account. Therefore, a good understanding of the coastal zone is needed, but especially in data-poor regions there is a lack of geomorphological information such as slope and grain size. This thesis presents an alternative approach for acquiring this data with the ability to run on a global scale using remote sensing technologies. Remote sensing, satellite imagery in particular, has proven to be a promising new technology to monitor coastal regions at large temporal and spatial scales and is applied here to determine coastal slopes and sediment sizes. The approach has shown promising results and paves the way for an estimate of the grain size at beaches around the world. Some hurdles remain to be taken but the ability to do measurements of coasts around the globe using satellite imagery will change the way we study our coasts forever. This research is a major step forwards in the global monitoring of the world's coast, but only shows the beginning of the endless possibilities.

Today's coastal zones are densely inhabited as the majority of the world's population lives in these attractive areas. The shorelines in coastal zones are shaped by complex spatial and temporal variable interactions between natural forcings like changes in mean sea-level, tides, wave and wind conditions, and storm surges. Besides, natural hazards such as coastal erosion, tropical cyclones, hurricanes, typhoons, floods, salt intrusion and tidal surges threaten a major part of the world's population. Furthermore, climate change is likely to increase the risk of natural hazards. As a response, humans changed the world's shorelines and the forcing-driven processes that work on them, to increase protection against hazards and keep supporting their activities. These human interventions are deployed discontinued in time, disperse spatially and might result in negative consequences leading to human-induced hazards. This research focuses on one particular hazard to coastal communities, coastal erosion, which is extended to a term referred to as shoreline evolution by incorporating coastal accretion as well. Up till now, detailed local-scale studies are able to expose human and natural drivers of shoreline evolution and provide a possibility to make a step towards intentional rather than accidental coastal engineering. Digital imaging and, more recently introduced, satellite imagery proved to be a promising new technology to measure and monitor shoreline evolution at bigger temporal and spatial scales. Nevertheless, the opportunity to develop a model that exposes the drivers of shoreline evolution on a planetary scale remains unexplored. This is due to the required computational effort as well as the large variability in coastal systems around the world. With an ever-increasing data availability, data-driven models incorporating Machine Learning (ML) proved to be an efficient alternative approach to heavy computing classical process-driven models in civil engineering practice. Next to this, a coastal classification can be used as a means to inventory the aforementioned variability. Therefore, the research objective in this study is to explore the possibility of exposing and classifying the drivers of shoreline evolution on a planetary scale, by employing ML on satellite imagery. Approximately 390.000 km of shoreline is analyzed for the past 33 years. This resulted in a statistically derived classification of natural and human-induced sandy shoreline evolution. By elaborating on this classification, it is found that natural and human-induced shoreline evolution accounts for approximately 16 and 25% of the total of globally exposed and classified shoreline evolution signals respectively. All outcomes in this research can support detailed local scale investigations and therefore provide an enhanced opportunity to make a step towards intentional rather than accidental coastal engineering. Hence, it is concluded that the developed and applied methods that employ ML on satellite imagery can be used to expose and classify (in)direct human and natural influences on sandy shoreline evolution using spatial and temporal characteristics on a planetary scale. 57% of the shoreline evolution signals is present in a regime with complex and combined (compound) influences, which still requires (rather than supports) local scale investigations to determine the correct influence or driver. More research is required to elaborate on the opportunities that can enhance insight in the compound regime, to improve the obtained results and advance the applicability of this study.

Due to sea level rise and subsidence of land, coastal erosion is a serious problem in the Netherlands. And nourishments are common solutions to mitigate coastal erosion. Over the last decades, many studies have been focusing on individual nourishment performance to help us increase understanding of it. However, it is still not clear for us how the nourishments behave under different parameters (such as water depth of the nourishment crest, wave heights or nourishment size etc). This thesis tests the impact of different parameters on the erosion rate of the nourishments through measured data and numerical model simulations.

Due to climate change and sea level risethe coastal zones are getting exposed to increasing risks   like coastalrecession, putting in risk human lives and coastal infrastructure being worthbillions of dollars. Low lying countries like the Netherlands are consideredmore vulnerable to the effects of sea level rise. Large parts of the Dutchcoast have been eroding for centuries and nourishments schemes of approximately12 million m³ have been implemented annually in order to maintainthe coastline as it was in 1990. However, the future dune erosion will further increasedue to the impacts of climate change and hence the adaptation strategies shouldbe in line with the accelerated sea level rise and the possible effects thatmay bring. The most commonly used method to assess sealevel rise impacts on shorelines is the Bruun rule. However,Bruun rule’s deterministic nature cannot align with the risk-based framework thatcoastal zone management requires nowadays. This necessity initiated thedevelopment of a process-based model, the Probabilistic Coastline Recession(PCR) model, estimating the future coastal recessions in a probabilisticapproach. In this research, the PCR framework wasapplied at eleven locations along the Holland coast, in the Netherlands, underthree different SLR scenarios, the RCP4.5, RCP8.5 and Deltascenario. The availabilityof coastal profile data (from 1965 until now) and coastline position data (from1843 till 1980) made the Holland coast an ideal location to explore and extendthe applicability of the PCR framework. The most relevant assumptions for thiscoast were identified and explored. The recovery rate of the dune was a weakpoint of the PCR model and Holland coast was an interesting area to be tested.Three approaches of calibrating the natural recovery rate of the dunes werefollowed. In addition, the alongshore sediment transport which was assumednegligible to the previous case studies, in this work it was integrated intothe PCR model and pointed out that its contribution is important to the PCR.  For the eleven selected coastal profiles,20,000 simulations of 81 years (2020-2100) have been conducted and for everysimulation the most landward position of the coastline in every calendar yearhas been recorded. Hence, an empirical distribution of coastline recession forevery future year has been constructed. The ranges of the expected retreats in2100 (relative to 2020) for the different SLR scenarios are:0.5 m-155 m (for RCP4.5), 6 m-194 m (for RCP8.5) and18 m-172 m (for Deltascenario), corresponding to the 50 %exceedance probability values of the cumulative distribution function of thecoastline retreat. The average values of the coastal retreat for 2100 are 61 m,73 m and 97 m for RCP4.5, RCP 8.5, and Deltascenario respectively.The relevant average erosion volume by 2100 are 1664 m³/m,2005 m³/m and 2665 m³/m. According to thefindings, in 2100 the relative increase in volume loss along the entire theHolland coast is expected to be 95 %, 121 % and 173 %respectively for RCP4.5, 138 % for RCP8.5 and 195 % for Deltascenario.Finally, the results were compared to those raised from the Bruun rule method. Accordingto the findings, the majority of the profiles showing an erosive trend in thepast (before the ‘hold-the-line’ policy) raised slightly more conservativeresults when implementing the PCR model rather than when applying the Bruunrule method- especially under the Deltascenario. On the other hand, theBruun rule method is more conservative than PCR model for most of the accretiveprofiles. The PCR model can now be explored tolocations where the longshore sediment transport is not negligible. Theapproach followed in this study allows investigating the ability of the modelfor future coastal retreat estimates when a construction of a hard defence structureor a port may change abruptly the longshore sediment transport. Last, this studyadvances the PCR framework and can be a valuable assistance in the course offurther improving the model.  

Seaports are important maritime commercial facilities and key hubs for national and global trades. There is a growing need for seaborne transport and, hence, seaport facilities, especially in emerging economies due to economic and population growth, such as in Africa. In sedimentary environments, port construction may induce coastal impacts regarding up-drift accretion and down-drift erosion. These coastal impacts potentially increase risks such as harbour siltation and coastal area erosion. To mitigate or even avoid these risks in the pre-construction stage, coastline evolution around ports needs to be well-understood. Analysis of long-term shoreline position data around existing ports can provide this understanding. However, long-term in-situ shoreline position data around ports are often unavailable or inaccessible, especially in emerging economies which are often data poor. Nevertheless, nowadays a growing database of satellite images provides these data on a global scale for the last decades. Furthermore, the launch of Google Earth Engine cloud computing platform in 2016 enabled accessibility and efficient processing of these satellite images. This development allows building an evidence database for shoreline positions around African seaports. With this database, coastline evolution trends around all ports can be analysed, inter-compared and related to environmental and port characteristics to derive lessons for future port development.
According to the World Port Index, 165 African seaports (after excluding river ports, offshore platforms and anchorages from 266 African ports) are identified. Only 125 ports at sandy coastlines, where SDS detection has been validated, are focused in this research. The results from this study show that coastline evolution, especially coastal area erosion, around the majority of African seaports is limited by the narrow sandy beach and rocky substratum of Afro-trailing coasts. However, there are still some ports at sediment-rich coasts having dramatic erosion and/or siltation hazards. After analysing common characteristics of these high-hazard ports, lessons are learnt for port development. Regarding site selection and breakwater design, ports with massive river sediment supply and/or ports at open coasts have larger negative coastal impacts. For coasts with river sediment supply, it is better to construct ports at the up-drift side of the river mouth to avoid interruption of river supplied sediment transport, which is found helpful for ports around West Mediterranean Sea. Shoreline management plan should be coupled with methods to maintain or increase river sediment supply, which can be learnt from shoreline retreating around ports in West Africa. Furthermore, to reduce coastal impacts around ports, port breakwaters at open coasts should be carefully designed regarding length and orientation to achieve a smaller shore-normal projected length, especially when the gross longshore wave power is massive. Regarding mitigation methods for coastline evolution impacts, shoreline protection structures are effective in reducing erosion hazards in the time scale of 30 years. Extension of the port breakwater (s) can be a temporary solution to mitigate the potential siltation problem but to reduce the down-drift erosion problem at the same time, sediment by-pass system, which is proved to be successful in South African ports, can be designed.

Predictions of coastal morphology evolution are necessary to assess engineering solutions as well as understand coastal systems behaviors. Among the tools used to predict morphological evolution are the process-based models that make use of physical laws and empirical knowledge. Such models account for a considerable range of coastal processes and are rather complex, hence demanding substantial computational time. Usually, when using complex process-based models, reducing the size of the input parameters, named input reduction, is made necessary in order to reduce the computational effort.
The scope of the present study is to understand the influence of reduced wave climate on simulated morphological evolution. Input reduction algorithms, sequencing methods, number of cases and duration of the reduced wave climate were investigated and evaluated with a 1D (cross-shore) brute force simulation of 3.3 years in Noordwijk, Netherlands. Noordwijk is a wave-dominated sandy beach characterized by a double sandbar system that has an inter-annual net offshore migration. The assessment of the methods was carried out through cumulative skill scores, temporal evolution of profile perturbations (bars and troughs) and profiles at the end of the simulation.
Furthermore, the findings on the Dutch coast were validated with a 1 year-long, 1D (cross-shore) brute force simulation in Anmok beach, South Korea. Anmok is a wave-dominated sandy beach with crescentic bars in the nearshore morphological evolution.
One of the conclusions of the present study is that input reduction methods that have more control on the definition of bins such as pre-definition of wave height and wave direction bins perform better than methods that are based fully on the statistical properties of the wave dataset. Also, the order of the wave cases in the reduced wave climate must resemble at its best the natural variability of the full wave climate. Finally, a less robust reduced wave climate (with less representative wave conditions) applied in a smaller timescale performs better in terms of morphology than a more robust reduced wave climate (with more representative wave conditions) applied in longer time-scales.

Shore-normal breakwaters are constructed in coastal zones both for beach protection (erosion reduction) and port development (wave sheltering). These breakwaters have an effect on the waves, the (wave-driven) currents and hence, the sediment transport along the coast. The waves and tide force an equilibrium sediment transport along the coast in a natural unaltered coast. When breakwaters are constructed this sediment transport in the alongshore direction is (partially) blocked. This results in accretion at the updrift side of the breakwater and coastline retreat at the lee-side. Coastal erosion behind a breakwater can result in floods, destroy property or simply narrow the beach. Not only the short term effects of these structures (what happens during and short after construction) is important to know, also the long term effects need to be known; because the breakwaters are build for decades coastal influence of these breakwaters needs to be known for these time-scales as well. Therefore it is relevant to investigate the scale (in time and space) of these adverse effects beforehand.

In coastal engineering, numerical models are used to predict the impact of coastal constructions like breakwaters. High detailed models which take for all physical processes into account will result in accurate predictions but result in large computation times. Simplified models have smaller computation times and are more suitable for coastal impact predictions on larger spatial (10 - 100 km) and temporal (decades) scale. The objective of the thesis is to improve the coastline change predictions of models at decadal scale by reviewing the common practice and a new, very fast, module for lee-side wave computations and investigating different wave processes that can improve the wave modelling. This research will focus on (1) understanding of what wave processes are important for the coastline change and (2) advise on what models to use for which conditions and how to the improve model predictions.

The approach of this thesis is triple. First five different modelling approaches for wave modelling are compared, varying in computation time and usability, to a very accurate model that will be used as ground truth. The wave conditions will vary in direction and both wind waves and swell waves will be modelled. From this step de accuracy of these models for different wave conditions can be analyzed. The second step is to get an better understanding of the processes that are involved with the wave modelling. This information can be used to improve the accuracy of model approaches with high computation times. The last step is to see how this relates to sediment transport and thus the coastline change at the lee-side of a breakwater.

This can be concluded after the investigation of 3 wave model (SWASH, SWAN and the Kamphuis module)


SWAN model approach is for cases with wind waves a good model approach: the wave height, wave direction and the sediment transport are well representative. Also the setup differences are very similar to the ground truth model.
SWAN is not very accurate for cases with a small directional spreading. For these cases the diffraction is more important and there is not enough wave energy in the sheltered area nor is the wave direction well represented.
Kamphuis with Snellius (refraction) is for the case with a wide directional spreading, given the computation speed, pretty good. For the cases with a small directional spreading the wave height is not accurate.


The conclusions related to the influence of the wave processes are:


The refraction is investigated by comparing the Kamphuis module (which does not incorporate refraction) with the ground truth model. When Snell's law was applied to the kamphuis module the model results did show good results. Therefore the refraction is (especially outside the sheltered zone) very important to the wave direction.
There is a large difference for cases with small and large directional spreading. The SWAN model gives results can can be expected when no diffraction is present. Directional spreading both influences the wave height directly behind the breakwater as well as the influence lengt of the breakwater.
Diffraction does not play an important role for wind waves. The large directional spreading results in much smaller energy differences between the sheltered and the non-sheltered zone. Even SWAN without diffraction gives a good representation for wind waves. For swell waves however this is very different.Then diffraction is very important. There is a much larger difference in wave energy between the shelterd and the non-shelterd zone and for a good representation a model that can coop with diffraction is a must.
Current induced refraction of the waves has influences for waves of an incoming agle 0 to 30 degrees because this will result in rip currents along the breakwater that turn the waves up to an extra 10 to 15 degrees.



The relative sediment transport of the five modelling aproaches was also analyzed. The sediment transport proxy for the five modelling approaches showed that SWAN computes the wave height and direction well for wind waves. For swell waves however the model did not show good results. The reason is that there is no diffraction in SWAN, the diffraction computations with SWAN did not show much improvements either.

The sediment transportation proxy for the Kamphuis module with Snellius where expected to be good for wind waves, because the wave height and wave direction where well represented. However the length of the erosion pit was much smaller and also the shape of the erosion pit is very different from the other two models.