A multivariate, stochastic, climate-based wave emulator for shoreline change modelling. (October 2020)

• Record Type: Journal Article
• Authors: Cagigal, Laura; Rueda, Ana; Anderson, Dylan; Ruggiero, Peter; Merrifield, Mark A.; Montaño, Jennifer; Coco, Giovanni; Méndez, Fernando J.
• Is Part Of: Ocean modelling. Volume 154(2020)
• Publication Date: 2020-10
• Subjects: Weather types -- Emulator -- Chronology -- Coastal hazards -- Shoreline; Oceanography -- Periodicals; Océanographie -- Périodiques; Oceanography; Periodicals; 551.46
• DOI: 10.1016/j.ocemod.2020.101695
• Languages: English
• ISSNs: 1463-5003

Abstract

Abstract: Coastal hazards often result from the combination of different simultaneous oceanographic processes that occur at multiple spatial and temporal scales. To predict coastal flooding and erosion, it is necessary to accurately represent hydrodynamic conditions. For this reason, here we present a stochastic, climate based wave emulator that provides the hydrodynamic conditions needed for these predictions. The emulator can generate an infinitely long data series maintaining its statistical properties at different time scales, from intra-storm to inter-annual variability, and its link to large scale climate patterns. The proposed methodology relies on the use of weather types and an autoregressive logistic regression model forced with different variables to simulate daily scale chronology. Considering the dependencies of wave conditions on the different weather types, the intra-storm chronology is solved by means of shuffling and stretching historical wave sequences. To demonstrate the replicability of this emulator worldwide, we have applied the model to 3 different locations and found good agreement when compared to the historical data. Furthermore, to illustrate and explain the strengths and limitations of the emulator, we present a different application for each of the different locations. Highlights: A new stochastic climate-based wave emulator was developed as a tool for probabilistic assessments of coastal hazards. The emulator generates infinitely long data series maintaining historical properties at different time scales. The methodology can be reproduced worldwide and can be adapted to generate hydrodynamic conditions under climate change.