The following text is an extract of the extended abstract entitled "Parametric Characterization of Wave Climate along the Andalusian Coast for Non-Stationary Stochastic Simulation" whose authors are Manuel Cobos, Pedro Maga��a, Pedro Oti��ar and Asunci��n Baquerizo, and that was included in proceedings of 39th IAHR World Congress where this dataset is included. Processed data comes from PIMA Adapta Costas project (Ram��rez et al., 2019), in particular, from projections of maritime climate for 2026-2045 and 2081-2100. Sea climate contains, among other information, time series of the significant wave height (Hs) obtained for several combinations of GCM-RCM projections of EUR-11 for the RCP 8.5. GCM-RCM combinations ACCE, CMCC, CNRM, GFDL, HADG, IPSL, MIRO with a 0.1 degrees grid were used for the Atlantic facade while CNRM, HADG, IPSL, MIRO, MEDC, MPIE, ESM2, EART models with 1/11 degrees were used for the Mediterranean one. A total of 210 locations were analyzed, 54 at the Atlantic facade and 156 at the Mediterranean one (Figure 1). The data was bias adjusted using the Empirical Quantile Mapping (D��qu�� et al., 2007; Michelangeli et al., 2009). Information of the significant wave height and the dependence between the values at a given time with previous values with a VAR(q) model is already available. At each location, the methodology of Lira-Loarca et al. (2021) was applied, using the software described in Cobos et al. (2022a). More precisely, for every GCM-RCM (hereinafter, model n for n = 1, .., N where N = 7 for Atlantic data and N = 8 for the Mediterranean data), a non-stationary marginal distribution of Hs, , assuming that the year was the largest periodicity of the climate, was fitted to data using a lognormal model for the central part and two generalized Pareto distribution for the lower and upper tails, as in Solari and Losada (2011). The non- stationarity is considered by assuming a decomposition of the parameters of the distribution and of the percentiles of the common end points of the interval into a trigonometric truncated expansion. In addition, the coefficients of the matrix, Cn, of a VAR(q) model with q up to 92 hours were estimated. The ensemble multi-model characteristics of the data were obtained from the compound distributions and the weighted averaged matrix coefficients. Soon, the results of the peak period (Tp) and mean incoming wave direction (��m) and the coefficients of the multivariate VAR model will also be included.

This work has been developed within the framework of the following projects: (1) Work for the study of flooding and erosion in coastal areas of Andalusia in a climate change scenario [CONTR2018/66984], and (2) Risk management associated with erosion and flooding in a climate change scenario and decision-making on concessions in the maritime-terrestrial public domain [CONTR2020/194906 The first author is also indebted to Consejer��a de Transformaci��n Econ��mica, Industria, Conocimiento y Universidades de la Junta de Andaluc��a (POSTDOC_ 21_00724) which partially funded his work

{"references": ["Cobos, M., Maga\u00f1a, P., Oti\u00f1ar, P. and Baquerizo, A. (2022). Parametric Characterization of Wave Climate along the Andalusian Coast for Non-Stationary Stochastic Simulation. In proceedings of 39th IAHR World Congress"]}