Shape investigation and probabilistic representation of coastal storms. Applications to Mykonos and Barcelona

A Non-Stationary and Directional Probabilistic Analysis of Coastal Storms in the Greek Seas

“…The storms identification and representation process for each directional sector through the implementation of the three thresholds (see, e.g., [47,48]), namely the 0.95 quantile for H m0 , 6 h for the storm duration, and 12 h for the calm period between consecutive storms, as well as the use of the easy-to-apply automated H m0 0.95 quantile for the GP threshold, provides an adequate number of storms data for each directional sector. These adopted values for the three thresholds, when aiming to identify storm events, are in line with several research studies, such as [15,20,21]. Additionally, the non-parametric percentile or quantile method for H m0 used here for the GP threshold value (e.g., [52,53]) can be applied to multiple datasets and locations [30].…”

“…It is widely accepted that the identification of coastal storms is achieved through the use of three different thresholds: (a) the first one corresponding to the significant wave height; (b) the second one to the minimum storm duration; (c) the third one to the calm period between successive events (e.g., [47,48]). Following [20,21], the threshold of the significant wave height is defined as the 0.95 quantile of the significant wave height at each specific location, whereas the other two thresholds are determined based on the regional characteristics.…”

“…The variable threshold value between the directional sectors enables a directional extreme value analysis (DEVA) to be performed, even in sectors that present milder sea conditions than other sectors but present some extreme events compared to the data belonging to these sectors. Additionally, the minimum calm period is selected to be 12 h (following [20,21]), while the minimum duration is selected to be 6 h (e.g., [15]). This Following Bender et al [44], the time window length should satisfy two fundamental conditions: (1) it should be adequately short for the assumption of stationarity to be quite sound; (2) it should also be large enough so that the fitted probability distribution can represent the data properly and reliably.…”

“…The variable threshold value between the directional sectors enables a directional extreme value analysis (DEVA) to be performed, even in sectors that present milder sea conditions than other sectors but present some extreme events compared to the data belonging to these sectors. Additionally, the minimum calm period is selected to be 12 h (following [20,21]), while the minimum duration is selected to be 6 h (e.g., [15]). This variable threshold technique could be in accordance with the variable threshold parameter value of the generalized Pareto probability distribution often applied to a DEVA (see [34,35]).…”

“…These studies usually use a non-stationary extreme value analysis (NS-EVA) that is an extension of the more conventional stationary extreme value analysis (EVA). The EVA and NS-EVA are widely applied (e.g., [18][19][20][21]) and provide useful tools for numerous applications, such as the design and reliability and resilience assessment of marine and coastal structures (e.g., [22][23][24][25]), storm-induced coastal flooding (e.g., [26,27]), and coastal erosion (e.g., [28,29]), as well as the design of wave energy facilities (e.g., [30]). It is noted that an EVA assumes that the underlying probability distribution of the data remains constant over time [31].…”