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.

Several alternatives to the Rayleigh distribution have been proposed for describing individual wave heights in regions where depth-induced wave breaking occurs. The most widely used of these is the so-called Battjes and Groenendijk distribution. This distribution has been derived and validated in a context of a shallow water foreshore-waves propagating over a gently sloping shallow region towards the shore. Its validity for waves propagating in regions with shallow flat bottoms is investigated here. It is concluded that the distribution on average underestimates (outside its range of validity) high wave height measurements in shallow flat bottoms by as much as 15%.

Metocean studies involve a lot of statistical analyses in the fields of data validation, determination of normal and extreme metocean conditions and data transformation. All these analyses can be performed in different ways. Deltares' best practice guidelines to execute metocean studies in an efficient and standardized way are summarized. Special attention is paid to the execution of extreme value analyses, for which procedures based on the extreme value theory are recommended.

Several alternatives to the Rayleigh distribution have been proposed for describing individual wave heights in regions where depth-induced wave breaking occurs. The most widely used of these is the so-called Battjes and Groenendijk distribution. This distribution has been derived and validated in a context of a shallow water foreshore-waves propagating over a gently sloping shallow region towards the shore. Its validity for waves propagating in regions with shallow flat bottoms is investigated here. It is concluded that the distribution on average underestimates (outside its range of validity) high wave height measurements in shallow flat bottoms by as much as 15%.

This paper compares three main methods for estimating extreme wave loads with a view towards determining the sensitivity of estimates to the particular approach chosen. The approaches considered include: a) The generally used ad-hoc procedure of performing an extreme value analysis of the H_s data, trying to find a relationship between wave height and period at the storm peaks and then, once the return values of extreme wave heights are estimated, estimating the associated return value of the wave period by means of the relationship found, b) The 'structure variable method' in which the pairs wave height and period observations are converted into univariate loads to which univariate extreme value theory is applied to estimate the return value of the structural load, c) The multivariate extreme value approach suggested by [1] in which a 'multivariate return value', namely the most probable value of the wave period conditional on a return value of the wave height, is estimated. Our study is based on a 44-yr long timeseries of wave conditions created using the shallow water wave model SWAN and calibrated ERA-40 data. The results suggest that the three approaches yield similar estimates. However, the ad-hoc procedure a gives the least conservative estimates. Approach c provides results that apply to any choice of load function and which to a certain extent are independent of the location in which the estimates are obtained, for which reason it may generally be the preferred one.