Suchergebnisse

Abstract. Sandy beaches and dune systems have high recreational and
ecological value, and they offer protection against flooding during
storms. At the same time, these systems are very vulnerable to storm
impacts. Process-based numerical models are presently used to assess the
morphological changes of dune and beach systems during storms. However, such
models come with high computational costs, hindering their use in real-life
applications which demand many simulations and/or involve a large
spatial–temporal domain. Here we design a novel meta-model to predict dune
erosion volume (DEV) at the Dutch coast, based on artificial neural networks
(ANNs), trained with cases from process-based modeling. First, we reduce an
initial database of ∼1400 observed sandy profiles along the
Dutch coastline to 100 representative typological coastal profiles (TCPs).
Next, we synthesize a set of plausible extreme storm events, which
reproduces the probability distributions and statistical dependencies of
offshore wave and water level records. We choose 100 of these events to
simulate the dune response of the 100 TCPs using the process-based model
XBeach, resulting in 10 000 cases. Using these cases as training data, we design a
two-phase meta-model, comprised of a classifying ANN (which predicts the
occurrence (or not) of erosion) and a regression ANN (which gives a DEV
prediction). Validation against a benchmark dataset created with XBeach and a
sparse set of available dune erosion observations shows high prediction
skill with a skill score of 0.82. The meta-model can predict post-storm DEV
103–104 times faster (depending on the duration of the
storm) than running XBeach. Hence, this model may be integrated in
early warning systems or allow coastal engineers and managers to upscale
storm forcing to dune response investigations to large coastal areas with
relative ease.