Data Reduction Techniques of Coral Reef Morphology and Hydrodynamics for use in Wave Runup Prediction

Data Reduction Techniques of Coral Reef Morphology and Hydrodynamics for use in Wave Runup Prediction

Scott, Fred (TU Delft Civil Engineering & Geosciences)

Reniers, A.J.H.M. (mentor)
McCall, Robert (mentor)
Pearson, S.G. (mentor)
Antolínez, José (mentor)
Storlazzi, Curt (mentor)

Delft University of Technology

Coastal and Marine Engineering and Management (CoMEM)

2019-07-17

Many tropical, coral reef-lined coasts, are low-lying with elevations less than five meters above mean sea level. Climate-change-driven sea level rise, coral reef decay and changes in (storm) wave climate will lead to greater chance and impacts of wave-driven flooding, posing a heavy threat to these coastal communities. Early warning systems (EWS) are effective for risk management and disaster reduction, however, the vast majority of the world's inhabitants of coral reef-lined coasts have no such system in place. Unfortunately, the complex hydrodynamics and bathymetry of reef-lined coasts make it difficult to establish a global flood prediction model for these areas. This thesis aims to develop a set of 'cluster profiles' that can be used to accurately represent coral reef-lined coasts around the globe. By representing an expansive variety of reef morphology, the cluster profiles are capable of predicting the wave runup over thousands of different coral reef profiles with a fraction of the number. The cluster profiles could be input into a tool such as a Bayesian probabilistic network which can be trained to provide real-time wave runup and flooding predictions given local bathymetry and offshore wave conditions, thus establishing a simplified global flooding EWS. The methodology includes two stages of data reduction. First, cluster analysis techniques are used to group thousands of coral reef profiles into 500 clusters based on morphology alone. Second, agglomerative hierarchical clustering is used to further group the profiles with similar morphology and wave runup response, resulting in a final set of 311 to 45 cluster profiles. Here we show that the cluster profiles are capable of predicting the wave runup for a set of 1000 reef profiles with a mean relative difference of approximately 10\%. The comparison was done using the numerical wave model XBeach with four different wave conditions. The methodology has been developed such that it could be expanded to other coastal environments. A summary of the methodology used in the study is illustrated on the following page.

Data reduction
Cluster analysis
Data mining
Coral reefs
Low-lying islands
Coastal flooding
Early warning systems
Climate change
XBeach

http://resolver.tudelft.nl/uuid:ce4e3e15-fae2-4bca-9fe2-389505433a5f

Student theses

master thesis

© 2019 Fred Scott