PROTOCOL project (https://gdfa.ugr.es/procotol) addresses this topic with the purpose of developing an assessment methodology and establishing technical recommendations for the design of coastal protections on urban fronts. The methodology is mainly based on the following components: (1) quantification of agents and actions considering their different scales of affection and the predicted scenarios (projections) of sea level rise; (2) risk assessment at the coastal urban front; and (3) calculation of the overtopping based on the types of protection and the envisaged scenarios. This methodology will be applied to five study areas in Spain, Mexico, Portugal and Uruguay. The results obtained for the different sites will allow contrasting the effects of the global warming in different parts of the world and will serve as valuable examples of the products derived from this project.

The tool consists of two main blocks, the first centered on data entry and pre-processing, and the second on data analysis. In turn, each of them is divided into three different modules, as shown in the Figure 1. The first module of the pre-processing block allows the reading of data from various sources, from major European databases such as Copernicus to numerical model outputs such as WAVEWATCH III. The tool then performs a quality analysis of the data and makes use of processing functions that have been specifically designed and incorporated for gap-filling or null value detection, among others. Since the tool reads data from multiple sources with different formats, it is necessary to homogenize these formats into a standard one so that the rest of the modules can work independently from the source of the original data. The developed format, named as MetOcean DataFrame, is composed by a Pandas DataFrame plus a series of attributes such as the location, the depth or the forcing agent among others.

The analysis block is in turn composed of three other modules. The first module summarizes the basic climate analysis, including histograms and density functions of the main variables and their correlations, as well as a complete summary of the data, among other options. The tool allows both the automatic graphic representation of the results and the saving of this information with different output formats. The second module performs an average regime of the different variables, adjusting different theoretical distribution functions to the data. The tool gives the user flexibility to select different types of functions and to analyze the fit according to goodness-of-fit. Finally, the third module performs an extreme analysis of the variables using the "Peak Over Threshold" (POT) method.