Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis
Paweł Terefenko (University of Szczecin)
D Paprotny (GFZ Helmholtz-Zentrum für Geoforschung, TU Delft - Hydraulic Structures and Flood Risk)
Andrzej Giza (University of Szczecin)
O Morales-Napoles (TU Delft - Hydraulic Structures and Flood Risk)
Adam Kubicki (GEO Ingenieurservice Nord-West)
Szymon Walczakiewicz (University of Szczecin)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Cliff coasts are dynamic environments that can retreat very quickly. However, the
short-term changes and factors contributing to cliff coast erosion have not received as much attention as dune coasts. In this study, three soft-cliff systems in the southern Baltic Sea were monitored with the use of terrestrial laser scanner technology over a period of almost two years to generate a time series of thirteen topographic surveys. Digital elevation models constructed for those surveys allowed the extraction of several geomorphological indicators describing coastal dynamics. Combined with observational and modeled datasets on hydrological and meteorological conditions, descriptive and statistical analyses were performed to evaluate cliff coast erosion. A new statistical model of short-term cliff erosion was developed by using a non-parametric Bayesian network approach. The
results revealed the complexity and diversity of the physical processes influencing both beach and cliff erosion. Wind, waves, sea levels, and precipitation were shown to have different impacts on each part of the coastal profile. At each level, different indicators were useful for describing the conditional dependency between storm conditions and erosion. These results are an important step toward a predictive model of cliff erosion.
help_outline