Mangroves play a vital role in coastal ecosystems by dissipating wave energy, protecting against erosion and supporting biodiversity. Despite their importance, quantification of their attenuation capabilities remains challenging. This study explores the potential of a two-dimensional (2DV) OpenFOAM model to simulate wave attenuation through mangrove forests using a layered approach to capture the vertical variability in mangrove frontal area.

The model is first validated against flume measurements under a range of different hydrodynamic conditions to assess its capability in a controlled, well-measured environment. The goal is that by applying the Reynolds Averaged Navier Stokes equations and a layered approach, a more realistic bulk drag coefficient can be calibrated for different hydrodynamic conditions. One of the objectives is to explore whether this approach can yield a stronger correlation between the bulk drag coefficient (Cd) and the Keulegan-Carpenter (KC) number.

Then, the model is applied to a real-world case study for the Mandai mangrove forest on the north-west coast of Singapore. his application demonstrates the model’s ability to simulate complex coastal environments, including variable bathymetry, forest extent, and wave conditions. A sensitivity analysis is also conducted to evaluate the effect of different parameters on the modelled wave attenuation

Results confirm the model's capability to accurately represent the vertical variations in vegetation structure and their influence on wave attenuation. It showed great agreement with the measured wave attenuation of the flume after calibration. Despite its expected improvement on the Cd-KC relation, it did not succeed in this, but it does reveal the impact of depth-variable drag forces and their effect on the velocity profile and wave attenuation. Furthermore, the case study showed its applicability to real-world coasts. Despite numerical dissipation standing in the way of any quantitative analysis, the runs in combination with the sensitivity analysis provided valuable insights into the model's behaviour and wave attenuation predictions.

This report details the development of a Flood Early Warning System (FEWS) for the Tana Basin in Kenya, executed by a multidisciplinary team from the Delft University of Technology. Recognizing the Tana Basin’s vulnerability to flood risks, exacerbated by climatic variability, limited funds, and limited available data, the project proposes a model that combines computationally efficient hydrological and hydrodynamic modelling with robust stakeholder collaboration. The study area comprises the entire Tana Basin, with a specific focus on the flood-prone area near Garissa used for validation. The FEWS developed incorporates local and scientifi-cally derived knowledge to forecast floods, aiming to aid the transition from a technologically intermediate to a technologically advanced FEWS. Through an iterative process of model selection, validation, and stakeholder feedback, the system attempts to integrate the GR4J hydrological model in SuperflexPy and combines this with the Super Fast INundation of CoastS (SFINCS) model. Data sources include global remote sensing datasets like FABDEM & CHIRPS. Furthermore, it uses the water level gauge data provided by the Water Resource Authority of Kenya, as well as TAHMO weather station data.

The report concludes by reflecting on the modelling techniques for both the hydrological and hydrodynamic models and provides recommendations for the further development of a FEWS in the Tana Basin in Kenya. The implementation of the hydrological model was not able to propagate external flows through the network, making it poorly suited for use in the Tana Basin. The hydrodynamic model works decently well in flood conditions but overpredicts flooding during regular flow conditions. Recommendations on stakeholder engagements and data-sharing practices to foster a resilient flood management system in the Tana Basin include more comprehensive Memoranda of Understanding (MoU) and stricter adherence to the Disaster Risk Management Framework of the United Nations.