Performance Evaluation of Wave Input Reduction Techniques for Modeling Inter-Annual Sandbar Dynamics

Abstract

In process-based numerical models, reducing the amount of input parameters, known as input reduction (IR), is often required to reduce the computational effort of these models and to enable long-term, ensemble predictions. Currently, a comprehensive performance assessment of IR-methods is lacking, which hampers guidance on selecting suitable methods and settings in practice. In this study, we investigated the performance of 10 IR-methods and 36 subvariants for wave climate reduction to model the inter-annual evolution of nearshore bars. The performance of reduced wave climates is evaluated by means of a brute force simulation based on the full climate. Additionally, we tested how the performance is affected by the number of wave conditions, sequencing, and duration of the reduced wave climate. We found that the Sediment Transport Bins method is the most promising method. Furthermore, we found that the resolution in directional space is more important for the performance than the resolution in wave height. The results show that a reduced wave climate with fewer conditions applied on a smaller timescale performs better in terms of morphology than a climate with more conditions applied on a longer timescale. The findings of this study can be applied as initial guidelines for selecting input reduction methods at other locations, in other models, or for other domains.

A Clustering Approach for Predicting Dune Morphodynamic Response to Storms Using Typological Coastal Profiles: A Case Study at the Dutch Coast

“…Input reduction in coastal applications has been commonly applied to reduce model forcing (Walstra et al, 2013;Chiri et al, 2019;de Queiroz et al, 2019) for long-term morphodynamic cases. Recently, Scott et al (2020) presented a methodology to reduce a large dataset (>30,000) of coral reef topo-bathymetric profiles to a representative subset (on the basis of wave runup) of 50-312 profiles, using machine learning, statistics and a numerical model.…”

A multi-model ensemble to investigate uncertainty in the estimation of wave-driven longshore sediment transport patterns along a non-straight coastline

“…Hence, this strategy usually underestimates extreme conditions and overestimates calm conditions. To overcome this issue, the forcing parameters can be weighted (see de Queiroz et al, 2019) to push the algorithm to a side of the cloud of data (e.g., towards extreme conditions) with the risk of missing data located at the other side of cloud of data (e.g., calm conditions). The performance of traditional look-up tables is usually dependent on the samples selected as the boundary condition of a wave model.…”

“…However, the high computational costs of spectral models are generally prohibitive for simulation of full time series of waves and local winds. To overcome this issue, different approaches such as traditional binary look-up tables (e.g., Vieira Da Silva et al, 2018), energy flux method (e.g., Benedet et al, 2016) and machine-learning-based techniques (e.g., Antolínez et al, 2016) have been developed for calculating wave transformation with reasonable computational costs (see de Queiroz et al, 2019 for more details). The obtained nearshore wave forcing could be introduced to different classes of LST models, including bulk formulae (e.g., Shaeri et al, 2020) and process-based models (e.g., Tonnon et al, 2018), to estimate variations of littoral drift in time and space.…”

Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction

“…The goal is to have the objects within each group more closely related to one another than to objects assigned to different clusters (Friedman et al, 2001). Machine learning techniques have been successfully applied for identifying patterns in different fields and processes including: waves (Antolínez et al, 2016), ocean currents (Chiri et al, 2019), sediment transport (Antolínez et al, 2018), sediment distribution (Antolínez et al, 2019), profile morphodynamics (de Queiroz et al, 2019), and atmospheric conditions (Rueda et al, 2019a).…”