Coastal regions, particularly beaches, are vital and dynamic areas for human activities. They serve various functions and are crucial barriers safeguarding coastal cities. However, the beach faces an increase in population and natural hazards caused by climate change are creating new challenges that coastal engineers are working to address.

This study introduces a new technique, SHORECAST (Satellite-derived Historical and future Orientation-based Relation for Estimating Coastline Adjustments and Sediment Transport), to enhance the Satellite-derived Shorelines (SDS). This technique is designed to estimate the shoreline dynamics in front of the shoreline and predict shoreline positions globally, offering a quick and accessible alternative to the existing models. The primary objective of this study is to employ shoreline position data obtained from SDS to estimate Longshore Sediment Transport (LST) and predict shoreline position quickly and effortlessly.

To achieve the study's objective, SHORECAST is developed and adapted into a multi-step framework using the annual dataset from the Shoreline Monitor of Luijendijk et al. (2018). The dataset assesses sandy and non-sandy beaches and their historical shoreline position at transects every 500 meters along the coast for the last 37 years. Only sandy shoreline evolutions are considered for the development of this new tool. First, a routine is developed to find coastal cells to which the research's aim could be applied. Secondly, multiple algorithms were deployed on the coastal cells to obtain the historical shoreline orientation and the LST. Combining these values leads to a correlation for predicting shorelines.

Out of all the coastal cells studies, three have been chosen: Nouakchott (Mauritania), Aveiro (Portugal) and Delfland (the Netherlands). The Nouakchott cell was split into a north and south side. These cells have in common that they all have a littoral barrier at one of the boundaries of the coastal cell with the assumption that there is no sediment transport. Due to this assumption, the LST could be calculated for each coastal cell. Nouakchott North experienced an annual sediment transport of 0.66 million m3 between 1985 and 2020, a volume increase of 23.01 million m3. Conversely, Nouakchott South experienced an erosion rate of 0.92 million m3 per year, resulting in a total loss of 32.36 million m3 over 35 years. In this same period, the Aveiro shoreline has accumulated a volume of 17.83 million m3 of sediment. In addition, the Aveiro shoreline displayed significant fluctuations compared to Nouakchott.

The Delfland coastal cell is the most complex coastal area among the three selected cases due to two littoral barriers at the boundaries and the anthropogenic measures in the past 37 years. This results in a volume increase of 45.55 million m3, similar to the beach nourishment volume of 46.51 million m3. As a result, it can be concluded that only the beach nourishment is visible in the SDS data, even though shoreface and dune nourishments have also been carried out during this period.

The findings indicate that the SHORECAST model, which incorporates the "Single-line theory" and specific boundary conditions, can generate multiple predictions. This makes it a universal tool for estimating sediment transport over time, even with future anthropogenic measures. It is important to note that not all assumed zero boundaries in sediment transport are zero in reality. Apart from SDS data, other shoreline position data can be integrated into the model to achieve similar results. Further research is needed to explore the possibilities of improving the understanding of different boundary conditions, thereby enhancing the obtained outcomes and the practicality of this study. An important suggestion is to explore the feasibility of identifying littoral barriers and other boundary conditions. This would make the developed model more robust and widely applicable.

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.  

A low-lying country as The Netherland is prone to coastal flooding, and its risk may be enhanced by global-warming induced climate change. Sea level rise has been historically considered as the key factor in coastal retreat, but waves also play an important erosive role along the coast, which can also be affected by the changing climate. During the last years, important advances have been achieved in climate modeling, with a very detailed characterization of the different components of the climate system, for the present and for different future scenarios. However, characterization of future ocean waves is still a matter of discussion and ongoing research. In this thesis, a statistical downscaling methodology based on weather types has been chosen to model the present wave climate and explore potential changes in future waves. These changes are quantified in terms of the impact of these variations in the longshore sediment transport. The methodology is applied to Noordwijk, selected as a representative location of the central Dutch coast. The statistical downscaling methodology is based on a classification procedure of the predictor into similar atmospheric patterns over the wave generation areas, namely the weather types. Then, the wave data is grouped according to the occurrence of the weather types. The predictor is built from the sea level pressure fields (SLP) and the squared SLP gradients, while the predictant wave climate is characterized by significant wave height, wave peak period and mean wave direction of wind sea and swell components, resulting in unimodal or bimodal sea states. The chronology of the weather types is modeled using an autoregressive logistic model, which incorporates the seasonality, the interannual variability and the persistence observed from the historical data. For each weather type, wave parameters are modeled using the categorical distribution for the sea-state type, non-parametric kernel density functions for the central-mass regime and two generalized Pareto distributions for the lower and upper tails of wave height and wave period data. The statistical dependence between wave parameters for each sea state is included using a vine-copula approach, where the bivariate dependence of H_s and T_p is modeled using the AC skew t copula and the remaining relations are considered to be Gaussian. The effect of climate change is studied using SLP predictors from the global circulation model ACCESS1.0, under the RCP8.5 scenario and period 2070-2099. The statistical model is applied to identify changes in the occurrence probability of the weather types in the future. The importance of these changes are quantified in terms of the wave-induced longshore sediment transport using the process based model Unibest TC. The longshore sediment transport distribution for each weather type is computed and afterwards the changes in gross and net transports are estimated using the present and future probabilities of the weather types. In terms of the weather types, the results of this work suggest changes in the occurrence probability of the weather types in the future, with variations of ±60% with respect to present climate, and no relevant changes in the sequence of weather types, neither in terms of the transition probability matrix nor in terms of the persistence of each weather type. In terms of longshore sediment transport, significant changes are detected in the transport associated with some of the weather types. For the remaining weather types, the changes are in the order of the model uncertainty. Taking into account the contributions from all weather types, a net increase of the southward directed net longshore sediment transport with respect to the historical period is detected. This increase is driven by a decrease in northwards transport and a lower decrease in southwards transport. This result is in line with a poleward shift of the North Atlantic storm track reported in other studies. Most importantly, weather-type based climate classification has in this thesis been succesfully proven to be a reliable tool to analyze the wave climate at the location of interest. Furthermore, the statistical downscaling also provides a climate emulator that captures the climate dynamics at different time scales, which can be used for stochastic simulations of the atmospheric and wave climate, either for the recent past or future projections.