Assessing coastal erosion hazards at large spatial scales: Insights and uncertainties

Panagiotis Athanasiou

Research output: ThesisPhD Thesis - Research UT, graduation UT

319 Downloads (Pure)

Abstract

Coastal erosion of sandy coasts due to extreme storms or long-term changes in marine forcing is a serious natural hazard, that can directly damage coastal investments and increase flooding in low-lying coastal areas. Assessing erosion hazard at large spatial scales (from global to regional) can provide useful information for the identification of general trends and vulnerable hotspots or to guide coastal zone management and adaptation priorities at a more regional scale. So far, continental and global assessments of coastal erosion use specific geophysical datasets or assumptions to describe the coastline, without looking into the effects of these choices on the assessments. Additionally, a large spatial scale of assessment can impose computational constrains even at regional scales, where extreme storm impacts on dunes are assessed mainly with simple qualitative models.

In this research, a merged product of available global elevation and bathymetric data together with global wave statistics are used to create a global map of nearshore slopes. The variability of the estimated nearshore slopes is validated in a both qualitative and quantitative manner, using available coastal classifications and in-situ surveys, showing good agreement. The uncertainties related to the use of available geophysical datasets to describe the sandy coastline in continental scale assessments are further studied at the European scale. The effects of the nearshore slope description are studied in combination with different datasets on sandy beach occurrence, using future coastal erosion due to SLR as the diagnostic. An uncertainty analysis is performed to compare the distribution of uncertainty sources for coastal erosion projections during the 21st century.

Next, data-driven statistical methods are combined with process-based modelling to produce a fast meta-model, able to predict dune erosion during extreme events for the entire Dutch coast (260 km). Here a novel technique that reduces a dataset of 1,400 elevation profiles at the Dutch coast to 100 real-world representative profiles, called Typological Coastal Profiles is presented. This enabled the efficient creation of 10,000 synthetic training cases that were simulated with the well-established XBeach dune impact model. A meta-model based on artificial neural networks is created and trained with the previously described synthetic dataset. The meta-model needs just 10 morphological and hydrodynamic local profile characteristics and 4 offshore storm parameters as input and can produce an estimate of dune erosion volumes for the whole Dutch coast in a matter of seconds.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Kwadijk, Jaap C.J., Supervisor
  • Ranasinghe, Roshanka, Supervisor
  • van Dongeren, Ap R., Co-Supervisor, External person
Award date21 Sept 2022
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-5428-2
DOIs
Publication statusPublished - 21 Sept 2022

Fingerprint

Dive into the research topics of 'Assessing coastal erosion hazards at large spatial scales: Insights and uncertainties'. Together they form a unique fingerprint.

Cite this