Torrential rain from tropical cyclones can have a devastating impact, causing loss of life and billions in damages. To better understand the risk faced by coastal communities, it is important to estimate how often a tropical cyclone could occur and how much rainfall it will produce. One way to do this is by analyzing past storms and building parametric models of rainfall rates during tropical cyclone events. While many parametric precipitation models –such as the Bader model– exist, their accuracy remains limited and many challenges still need to be overcome. The most important challenges are output overestimation and a poor representation of rainfall over land. Therefore, this thesis aims to reduce these biases by answering the following research question: "How can the bias in the radial rainfall distributions of a tropical cyclone in Bader’s parametrized model be reduced and be used for reliable rainfall estimates both above land and the ocean?" To answer this question, several new data sources were introduced from the TRMM/GPM satellites and Stage IV to improve the Bader model. While this original model only predicted precipitation based on maximum wind speed (vmax), the updated model also considers pressure deficit ΔP. The results suggest that ΔP can be a useful parameter to reduce bias and improve accuracy. However, it also leads to larger uncertainty ranges. Next, four precipitation profiles were proposed. A profile where precipitation is constant for low maximum precipitation values based on the predicted total rainfall (area under the graph) was selected for further exploration. The new models are explored during a case study of Hurricane Florence. Both the ΔP and vmax based models produced satisfactory results, compared to the benchmark IPET model. Moreover, an alternative fit above land has been proposed, where the highest precipitation is simulated at the eye. The proposed land fit improved the median of the predictions based on both vmax and ΔP. The ΔP based model performed the best in the case study, however, no definitive conclusion could be reached upon which model is most suitable overall as more case studies would be required. Finally the updated model has been compared to the original Bader model. The new data ensured better representation over land, the overestimation of precipitation was reduced, and the model was applied with more confidence outside of the training data set. Consequently, results showed an improvement on its prediction capabilities. As a concluding remark, this research project highlights the importance of having insightful data to enhance the decision-making and risk management of natural hazards: a model that accurately quantifies uncertainty and the risks associated with a TC, representing a valuable tool for better understanding flood risk. Nonetheless, there are still several ways to further improve the modeled profiles (e.g., by including more data, introducing asymmetry or adding temporal autocorrelation).

Hurricanes impose great threats on coastal communities in terms of hazard and impact. Recent hurricanes like Harvey (Texas, 2017) and Idai (Mozambique, 2019) emphasize the global character of this threat. In the U.S., Hurricane Harvey tied with 2005's Hurricane Katrina as the costliest tropical cyclone on record, inflicting $125 billion in damage. Harvey hovered above the state of Texas for six days in 2017, making it the longest landfalling storm in Texas history. During this time, over 600 millimeters of rain had fallen in most of the Houston area, with extreme observations showing 1500 millimeters of rain. This, in combination with surge and river discharges, caused inundation for over one-third of Houston. Current generation coastal flood early warning systems are often not designed to take into account the compound effects of pluvial, fluvial and marine flooding (e.g. the ADCIRC + SWAN model deployed by the Coastal Emergency Risks Assessment group ignores pluvial flooding). Moreover, the advanced models applied in these systems are computationally demanding and can therefore not be used in probabilistic real-time forecasting applications in order to include uncertainty in meteorological conditions. Furthermore, despite the importance of tropical cyclone rainfall, this field is not completely understood. Tropical cyclone rainfall distributions are dependent on best track data parameters (e.g. storm motion velocity), but also on environmental elements (e.g. topography). Current modelling approaches are either computational inefficient (e.g. large-scale climate models), are location-specific or are dependent on parameters not always available in archives. Therefore, there is need for a simple generic parametric rainfall model. Main focus of this research was the derivation of a methodology for assessing the joint probability of fluvial, pluvial and marine flooding. For this study, a case study is carried out for Houston, Texas. The TCWiSE tool is used to generate a set of synthetic hurricane tracks based on historical data and a Monte Carlo sampling method. A derived parametrization of tropical cyclone rainfall is used to generate a spatial tropical cyclone rainfall field. Furthermore, a Delft3D-FM model is used to generate offshore water level time-series for the synthetic hurricanes. A combination of SFINCS and Delft-FIAT is used to make an assessment based on both hydrodynamics and exposure for every single generated hurricane. The model train is capable of carrying out a flood risk assessment, derive flood maps for given return periods (e.g. 1 in 100-year flood) and make an exposure assessment for the joint occurrence of pluvial, fluvial and marine flooding. This framework can potentially be a useful tool for future policy- and decision making.

Coastal protection is required to keep coastal areas protected during storm conditions. Coastal dunes are a natural defense against flooding and erosion from the sea against storms. Many insights have been obtained regarding the positive effects of coastal dune vegetation on dune formation and growth in the past. Several recent studies have demonstrated the ability of both aboveground and belowground dune vegetation to reduce dune erosion during storms. However, the impacts of dune vegetation during collision regime storm conditions are not taken into consideration in morphological numerical modeling. Consequently, it is unclear how to evaluate vegetation impacts during design assessments. Therefore, the goal of this research is to investigate the potential effect of dune vegetation on dune erosion during collision regime storm conditions and subsequently link this with dune rehabilitation projects. This research is divided into two parts. First, the capability of the numerical model XBeach to simulate the potential effects of dune vegetation during the collision regime is investigated. Four vegetation approaches are identified in the model, which could possibly represent different dune vegetation effects. Using beach-dune profiles and erosion volumes obtained from two wave flume experiments, the performance and sensitivity of the vegetation approaches are tested in XBeach. Thereby, a distinction is made between the effect of aboveground vegetation: hydrodynamic altering and belowground vegetation: soil stabilization. The model results show that XBeach is capable of simulating dune erosion with vegetation during collision regime storm conditions. This can be primarily attributed to the increase of the critical slope in the avalanching module. This approach represents the soil stabilization effects of belowground vegetation. The application of the root model was added for even better simulations. This approach accounts for the additional root cohesion provided by belowground vegetation by increasing the critical velocity for sediment pickup. The values to be used for both vegetation approaches could not be defined systematically yet due to the small number of experiments assessed and little research found in the literature. It is recommended to obtain more information about the effect of dune vegetation on the stabilizing effects and the critical slope for avalanching. The application of a higher roughness value and the vegetation module, which both represent aboveground vegetation by altering the hydrodynamics, have shown to contribute insignificantly to erosion reduction in the examined cases. The belowground soil stabilization approaches are applied in a case study to give a first indication of the applicability of XBeach at large-scale and in dune rehabilitation projects. An XBeach model of Beira, Mozambique was set up. A higher critical slope demonstrated profile evolution and erosion reduction in line with observations in the literature. During wave impact, the avalanching module is the most suitable approach to account for the erosion-reducing effect of belowground vegetation. The root model appears to be a more appropriate strategy for accounting for belowground vegetation during milder conditions and shorter storm duration. It was proven that XBeach has very good potential to evaluate the effectiveness of dune vegetation in the design phase. The case study illustrates that vegetation will significantly increase the erosion resistance of the flood dunes. The dunes with vegetation can handle an additional 20-30 centimeters design water level or 0.5-0.8 meter storm wave height without breaching compared to the same dune without vegetation. The design with vegetation has shown to be more robust. However, the approaches are based on different assumptions and limitations and therefore the results should be considered carefully. It is strongly advised to conduct more research in this relatively new study area. This study investigates and proposes a method for quantifying the advantages of vegetation on dune erosion reduction. The relevance and added value of mature and robust dune vegetation for a resilient coastal dune system and protection against erosion and floods are confirmed. As a result, the incorporation of vegetation in dune rehabilitation projects is promoted.

BaCla is a new stochastic parametric precipitation model to estimate rainfall associated with Tropical cyclones (TCs) in a computationally efficient way. It is validated for a number of calibration cases along with the current benchmark IPET deterministic method. The results of these models are compared with the observed StageIV-based rainfall. Two predictive parameters, the pressure deficit [hPa] (Δ𝑃) and maximum sustained wind speed [m/s] (vmax) are tested for the BaCla model. Frank copula is used by the BaCla model to predict the peak amount of rainfall. It employs the adapted Holland wind profile to create a 1D rain profile. Asymmetry may be added to make a 2D rain profile. The calibration study challenges the assumption that the radius of maximum wind speed (Rvmax) is equal to the radius of maximum precipitation (Rpmax) in the BaCla model. Additionally, it is observed that the radial fit overestimates rainfall in scenarios where the peak amount of rainfall is less than 2.8mm/hr. These observations lead to modifications in the relationship between Rvmax and Rpmax, the radial fit threshold, and the fitting coefficients of the adapted Holland wind profile for rainfall distribution in the improved BaClHa model. The new BaClHa model is verified with new and calibrated sets of TCs. The BaClHa model shows potential in achieving good accuracy along with global applicability at a limited computational expense.