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.

Every year hundreds of thousands of people are affected by natural disasters that occur due to various physical phenomenon. They include earthquakes, tsunamis, volcanic eruptions, and hurricanes. In this research the focus is on hurricane events and the impact they have on a community. The ability for a community to bounce back is defined by their disaster resilience which, in turn, depends on their ability to identify vulnerabilities and prepare for the inevitable. Within this line of research, one of the main challenges is defining local risk due to hurricane events, especially when considering more than one hurricane-induced hazard. Furthermore, the challenge becomes even greater when analysing risk in locations around the world where data availability is scarce. This study aims to clarify and improve the existing methodology to define multi-hazard risk due to hurricanes. This methodology is verified by applying it to the case study of St. Martin, in order to delineate hurricane risk on the island. St. Martin was chosen to investigate, as it is a Small Island Developing State in the Caribbean that recently suffered immeasurable damages during Hurricane Irma (September 2017), and is still struggling to recover. The two hazards that were considered, in the application of the methodology, were hurricane-induced winds and hurricane-induced coastal flooding. In the case of St. Martin, hurricane-induced winds were found to contribute to 98% of damages due to Hurricane Irma, when compared to the coastal flood damages. Coastal flooding was found to be due to both increased storm surge levels and wave-induced flooding, showing that neither one is negligible for a reef island like St. Martin. Storm surge variation around the island was found to be minimal due to the scale of the island, and the fact that storm surge was predominantly pressure driven. Validation of the models to simulate hazards and impact on St. Martin proved to be challenging. An unconventional data source was used to validate the flooding model, which included analysis of Twitter data of images posted during Hurricane Irma. This is an example of a solution of how to deal with data scarcity in hazard modelling. The risk assessment of St. Martin involved simulating synthetic hurricane track scenarios and determining their respective wind and flood damages on the island. Combining the respective damages was done by including a damage threshold to ensure combined damages did not exceed 100%. The applied framework resulted in a hurricane risk map of St. Martin indicating Expected Annual Damages per community. The intention of the improved methodology is to apply it to a hurricane risk prone region, like St. Martin, and to use the outcome to delineate hurricane risk. This indicates hot-spots in the region of interest and improvements to disaster resilience can be discussed. The approach of Build Back Better is highlighted in this research to show how this risk map can be interpreted and what the results mean. Three branches are discussed, namely building back stronger, which involves ensuring infrastructure can resist more extreme events in the future.