The composite dataset of the present-day Infralittoral Prograding Wedges (IPWs) in the inner continental shelf of the Campania region (Central-Eastern Tyrrhenian Sea)

This article reports on the dataset gathered following the census of 83 present-day Infralittoral Prograding Wedges (IPWs), surveyed on the inner continental shelf of the Central-Eastern Tyrrhenian Sea. The purpose of the census was to explore their bathymetric range and assess the observational laws governing this variability. The ensued dataset (Campania Region IPW Dataset, CRID) includes geographic, topographic and morpho-bathymetric indices, descriptive of each IPW and more, the exposure of each IPW to wave forcing (Geographical fetch, Effective fetch and extreme significant wave height, HS). In this work, histograms contribute to describe all the variables and highlight the dominant features of each IPW. Location maps univocally links the geographic position of each IPW to the appropriate attribute record in the dataset. Further, thematic maps illustrate eight wave fields obtained by offshore-to-nearshore transformation by as many sea states scenarios with 200-year return period. Such wave fields are used as sources for significant wave height representing wave conditions over each IPW. This dataset could be implemented with new measures at a broader scale, by following analogue procedures for measurements, to enlarge the observational scale on IPWs and improve the numerical models which might eventually derive by the analysis of this dataset.


Keywords:
Marine and terrestrial DEM Submarine depositional terraces Morphometric indices Significant wave high Effective fetch Southern Italy to describe all the variables and highlight the dominant features of each IPW. Location maps univocally links the geographic position of each IPW to the appropriate attribute record in the dataset. Further, thematic maps illustrate eight wave fields obtained by offshore-to-nearshore transformation by as many sea states scenarios with 200-year return period. Such wave fields are used as sources for significant wave height representing wave conditions over each IPW. This dataset could be implemented with new measures at a broader scale, by following analogue procedures for measurements, to enlarge the observational scale on IPWs and improve the numerical models which might eventually derive by the analysis of this dataset.  The Campania Region IPW Dataset (CRID) has been acquired from several data sources. The morphological indices of the 83 IPWs have been drawn from marine and terrestrial DEMs by applying geospatial algorithms available in GIS-environment (Global Mapper ®, ArcMap, 10.8). The "coastline elevation in the backshore" 100 m inland (m, asl) was measured from terrestrial DEM [1] . "Effective Fetch (EF)" and "Geographical Fetch (GF)", here considered as proxies of IPWs exposure to wave forcing, have been calculated by using GEBCO_2020 (Grid General Bathymetric Chart of the Oceans, 2020) [2] and the geographical location of IPWs. EF namely, is the portion of sea which generates wind-waves and is computed by considering both length and width of the generation area. It was calculated in a GIS environment (ArcMap, 10.8) by applying a procedure based mainly on viewshed and drawing line algorithms [3] . GF is a simplified parameter of the wave exposure and was measured along a single direction at right angles to the IPW edge [4] . Raw offshore wave data were supplied by pitch-roll type directional buoys operating off the Island of Ponza (central Tyrrhenian Sea). Available records of buoys of Ponza are from 1 July 1989 to 31 December 2014 as a part of the Italian Wave Network [5][6][7] . An extreme value analysis of offshore wave data was carried out, by selecting a value of the return period, T R , of 200 years and deriving extreme sea states in front of each IPW. Values of " H S " were extracted at the 50 m-isobath ( H S,50 ) and at each IPW edge ( H S,ROP ). Finally, the "relative sea level variation" at each IPW site in the last 2 ky was collected by literature references. Data format Raw, Analyzed, Filtered Description of data collection The CRID records measures derived by a detailed geomorphological analysis of shallow water and terrestrial DEM along the Campania Region (southern Italy). and waves analysis of temporal series of offshore wave data from the Ponza Buoy (central Tyrrhenian Sea). [7] ( continued on next page )

Value of the Data
• The data here presented allowed us to derive the observational law which governs the depth of the present-day infralittoral prograding wedgesin central-eastern Tyrrhenian Sea. • The study aims to provide clues to solve a non-trivial issue that deals with the reliability of IPWs as proxies for sea level. • Specifically, the reader could test the same procedure in different localities to widen the survey at a broader scale and strenghten the reliability of the observational law.

Data Description
A census of 83 near-shore Infralittoral Prograding Wedges (IPWs) was realized on the inner continental shelf of the Campania Region (Table 1 in Supplementary Material), encompassing a coastline about 480 km long ( Fig. 1 ). The location of each IPW, sequentially numbered, so as being univocally linked to its relative indices, is shown in figure 2.
Namely, the Campania Region IPW Dataset (CRID, Table 1 in Supplementary Material) includes for each IPW numerical indices, descriptive of morphometry, geography and wave climate, and a classification of the coast typology.
In Fig. 3 is graphically shown how the morphological indices on each IPW were measured. All the indices are then represented by frequency histograms (Fig. 4)  IPWs show GF and EF within 300 km, respectively; O) 42 IPWs are exposed to H S,50 between 5.5 and 7.5 meters, while P) 41 IPWs are exposed to the H S,ROP ranging 4.8 and 6.8 meters.

Morphological and geographical indices
Morphological and geographical indices ( Table 1 in Supplementary Material) have been acquired from digital elevation models (DEM) of terrestrial coastal zone [1] and marine domanis by applying geospatial algorithms available in GIS-environment tools. The original regional-scale marine DEM merges different data sets with spatial resolution of grid cell varying from 5 mside to of around 30 m -side [3] . In very shallow waters, the DEM is composed merging single and multibeam beam echo soundings (SBES and MBES) developed by IAMC CNR (now ISMAR CNR). Interpolations have been performed for the single beam bathymetric dataset mainly in the range -2/-12 m, where swath bathymetric data were not always available. Low-resolution bathymetric data are available at EMODnet Digital Bathymetry [8] .
The IPW indices measured by DEM ( Fig. 3 ) are: -Elevation in the backshore represents a numerical descriptor of the coastal morpho-type; this measurement is extracted from terrestrial DEM [1] , by picking up the altitude at 100 m distance inland from the coastline, in the opposite direction of the terrace growth; -IPW ROP depth is the average value of the water column, taken along-strike the IPW edge and computed by geospatial algorithms available in GIS-environment (Global Mapper ®, tool analysis measurements), and it is expressed in m bsl; -IPW edge-coast distance is measured by geospatial algorithms available in GIS-environment (Global Mapper ®) along the orthogonal line depicted from coastline to the edge of the terrace and it is expressed in m; -IPW length is the measure of the linear extent of the IPW edge (expressed in km); -FSA is the foreset slope angle calculated downslope from the terrace edge median point along the maximum deep of the slope and is computed by geospatial algorithms available in GISenvironment (Global Mapper ®, path profile, sub-path info). This value is expressed in °and percentage %; -TSA is the toplap slope angle along the maximum deep in the direction of terrace growth and is calculated by geospatial algorithms available in GIS-environment (Global Mapper ®, path profile, sub-path info). This value is expressed in °and percentage %; -IPW direction is the angle between the segment along the growth direction of the terrace and the North (it is complementary to Orientation) and it is expressed in °; -IPW orientation is the angle of the segment that joins the two extreme points of the terrace edge respect to the North, and it is expressed in °; -GF is automatically obtained by an algorithm that measures the length, along a single direction at right angle to the IPW orientation, from the terrace edge to the nearest opposing coastline; it is expressed in km; -EF is calculated in a GIS environment (ArcMap, rel 10.8). The input data are the GEBCO_2020 Grid, a continuous global terrain model of oceans and land with a spatial resolution of 15 arc seconds [2] and the geographical location of IPWs. The Effective Fetch, measured in km, represents a more refined version of GF, initially considered in [4] . EF is calculated by applying the following formula, derived by equation: EF = i GF i × co s n θ i i co s n θ i where θ i is set at 10 °; GF i is the geographical fetch along the seven directions; n is a coefficient proportional to the load attributed to the GF i (in our case n = 2) [9] .

Wave climate indices
Raw offshore wave data were supplied by pitch-roll type directional buoys operating off the Island of Ponza (Central Tyrrhenian Sea). The records are available since 1st July 1989 [5][6][7] , as a part of the Italian Wave Network. From 1989 to about 2002, the wave buoys collected 30 min of wave measurements every 3 h, but when in presence of wave heights greater than 1.5 m, the measurements were continuous. From 2002 to 31 December 2014, the wave measurements have always been continuous and the wave characteristics parameters refer to 30-min time intervals. The dataset comprises the wave height computed on the zero-order moment of spectral function. (H m0 ), the mean wave period (T m ) and the mean wave direction ( Dir ). For non-breaking waves, it can be assumed that H m0 ≈ H S .
A pre-processing phase focused on a gross stochastic error detection was applied. The data processing was firstly regarded the missing data problem. Missing values reduce the representativeness of the sample. Moreover, it can severely disturb the conclusions drawn from the data. About 10% of missed data of about 20 years of observation have been recognized. In order to get a conservative estimation in case of lack in the time series, missed data or values of wave height less than 0.2 m for several hours were considered as errors and removed. In addition, to test the sensitivity of the results, H S = 1 m and 2 m were also used. The sensitivity analysis showed that the estimated wave energy flux does not differ substantially if wave heights of 1 m or 2 m were used to fill the missed data. After the regularization procedure, taking into account missing data, unrealistic calm conditions and spikes, a virtual geographical transposition of the time series was applied, creating a virtual buoy located offshore of the Gulf of Napoli (40 °29 45.06 N; 13 °47 46.70 E; depth of 1037 m). For details in the application of the method, see [10] .
To define the intensity of rare storm conditions (i.e. hours of Mediterranean hurricane, otherwise known as "medicane"), according to current coastal engineering practice, Extreme Value Analysis were carried out. The last is a branch of statistics dealing with the extreme deviations from the median of probability distributions. Knowledge of the value of an extreme event for a given return period T R is the main result of the Extreme Value Analysis. Therefore, extreme events are described in terms of function H S (T R ) which links the significant wave height of a sea state with different return periods T R . To produce a set of offshore extreme significant wave height values, the Peak Over Threshold (POT) method was followed. According to current ocean engineering practice, the Weibull distribution was adopted as extreme value distribution: where a, b and c are the scale, position and shape parameters, respectively. In this work, these parameters were estimated by means of the last squares method. Then, the H S value for a given return period (in years) is computed as: Fig. 5. Wave field of significant wave height for extreme scenarios with 200-year return period coming from eight wave sectors. Letters refer to such scenarios, as denoted in paragraph 4.2 of [3] .