Small Island Developing States, including many low-lying atoll islands, are among the most vulnerable countries to natural hazards and climate change disproportionately amplifies this vulnerability. Hence, there is a strong need for disaster risk reduction and risk management. Further development and implementation of methodologies for flood hazard assessment of atoll islands contributes to this. The methodology proposed in this thesis was applied to Majuro, an atoll island and capital of the Republic of the Marshall Islands. More specifically, the flood hazard related to different flood drivers, and including compound events (i.e. the combination of coastal flooding and precipitation) was assessed for the densely populated Delap, Uliga, and Djarrit region, in the east of Majuro Atoll. Main flood drivers are waves during typhoon events and distantly generated (swell) waves, but precipitation and high water levels (mainly tide) are important as well. To include all possible combinations of these flood drivers, and to include the spatial variation in events, 1000 years of synthetic events was generated based on data for historical events. Accurate simulation of inundation depths was computationally unfeasible for all synthetic events. Hence, a method was developed to reduce the number of model simulations, without losing information on the probability of occurrence of each event. The main steps of this method seem applicable to many other study areas where many scenarios are needed to include all (combinations of) drivers. Main steps are: (1) Selection of representative events – by Maximum Dissimilarity Algorithm, based on parameters that characterize the events. Hereby, the most extreme events are included as well. (2) Simulation of inundation depths for the representative events – by use of Delft3D, SWAN, and XBeach models. The XBeach model included a module for rainfall (first application). (3) Weighted interpolation to obtain the inundation depths for the synthetic events – based on the same parameters as in step 1. Based on the inundation depths for 1000 years of synthetic events, flood maps for different return periods and flood drivers were derived. These provide insight in the flood hazard for the DUD region due to the different flood drivers and for different return periods. The importance of different flood drivers varies significantly per area. Generally speaking, flooding related to (swell) waves in combination with high water levels is more frequent, while infrequent typhoons lead to the most severe flooding. Precipitation is an important flood driver as well, and exclusion would lead to underestimation of the flood hazard. Analysis of inundation depths suggests that for many areas these are limited to a maximum, where after excess water drains to the ocean – mainly into the lagoon. The derived flood maps could be used as a base for assessment of flood risk, climate change impacts, and closely related freshwater availability. In relation to the latter, more in-depth understanding of the contributions of precipitation and coastal flooding to the total flood hazard is needed, as on the long term infiltration of precipitation seems favourable, while that of oceanic water is not.

Many small island developing states (SIDS) are among the most vulnerable to climate change (e.g. sea level rise) and seasonal to inter-annual climate variability, and subsequently experience flooding due to swell waves and wind waves, coastal erosion and salinisation of freshwater lenses. To counteract this, reef flat mining for sand and aggregate offers a possible solution to the material demand for coastal protection infrastructure and other engineering projects. However, the knowledge on the effects of these pits is limited to only two studies at a single reef: near-shore wave transformation based on measurements (Ford et al., 2013) and a numerical modelling study (Yao et al., 2016). This MSc thesis provides insights on the effects of pits, related to hydrodynamics and wave runup, on a large variety of fringing reefs, by using a 1D and 2DH process-based wave-resolving hydrodynamic model (XBeach non-hydrostatic+, “XBnh+”). Model results indicate that excavation pits cause a decrease in amplitude for waves in the infra-gravity (IG) frequency band. For the majority of the modelled reefs, this was mainly due to a modification of the longest natural frequency of the reef caused by the pit, resulting in a decrease in resonant amplification. Both pit width and cross-shore location have a strong influence on this mechanism. The observed changes in variance in the high frequency (HF) band can be partly explained by a decrease in wave dissipation, as well as a decrease in wave-wave (triad) interaction, both associated with (locally) increased water depth, which causes an increase of the HF peak and a decrease of the HF tail. Of all modelled reefs, there is a 15% chance of an increase in wave runup due to the presence of a pit. This probability is lowest for pits with narrow width and/or located close to the reef crest. The change in runup ranges from +10% to -20%. An increase in runup is caused by a combination of increased wave energy at peak frequencies, as well as smaller reductions in resonant amplification. Moreover, the effects of pits on mean water level near-shore of the excavation can cause circulation patterns that potentially result in coastal erosion and entrapment of sediments.

The Dutch coast is affected by coastal erosion. In order to limit erosion and prevent inland migration of dunes, sand nourishments have been adopted as a common practice for the maintenance of the coast. The evaluation of the applied coastal erosion policy is achieved by the use of coastal indicators. Increased number of nourishments affects the coastal profiles and coastal indicators are used to describe those changes along the coastal zone. The present work intends to improve the definition of one of the indicators (dune foot position) by proposing a new methodology and to develop a Bayesian network in order to assess the effectiveness of nourishments on the coastal system. Coastal indicators, such as the Momentary Coastline and the dune foot position, are used in the Bayesian network in order to quantify those effects.

Available field measurements of the entire Dutch coastline (JARKUS morphometric database) are used in order to develop the methodology of the dune foot position detection. Taking into account the geometry of the profiles, the dune foot position can be detected by calculating the first and second derivatives of the measured points along the profiles. Comparison with visual, in-situ observations, constitute the validation method of the proposed methodology. Root mean square errors, with respect to visual observations, are used to compare the performance of two methods for the dune foot position detection; the proposed methodology and the current dune foot definition, which detects the dune foot at the most seaward crossing of the profile with the level of +3 m NAP. By looking at the performance of the methodologies at different areas, the results are diverse. However, the proposed methodology shows a general improvement of the detection. Moreover, the methodology is believed to be generic and applicable to other dune systems around the world, since it is purely based on the morphology of the profile.

A Bayesian modelling approach is used to assess the effectiveness of nourishments on coastal indicators for the Holland coast. The selection of the input and output parameters, the design of the network and the optimization of the structure are the main steps for the development of the model. Once the optimum configuration is chosen and the model is trained, the Bayesian network gives the possibility to investigate the relations between variables, by constraining the input nodes on a specific range of values and assessing changes on the indicators. By applying those constraints it can be found that nourishments have positive effects on the coastal indicators, since larger seaward displacement is observed compared to cases in which no nourishment has been implemented. Moreover, initial erosive trends of the dune foot position are constrained by the implementation of nourishments. In addition, the effects of nourishments at different time scales are investigated. To this end, time horizons of 1, 5 and 10 years after a nourishment implementation are chosen. Positive effects of nourishments are present at all the considered time horizons, with beach nourishment to have an immediate effect on the indicators, whereas shoreface nourishments reach a stronger effect 10 years after the implementation.
Finally, the Bayesian inference can be used as a decision support tool for coastal managers, since its user-friendly environment can lead to easy interpretation and use for coastal management purposes. The required sand volume can be estimated in order to achieve a specific magnitude of seaward displacement of the indicators or in order to preserve the coastline at the current state. The estimations concern large spatial scales.