A new beach topography-based method for shoreline identification

The definition of shoreline is not the same for all contexts, and it is often a subjective matter. Various methods exist that are based on the use of different instruments that can determine and highlight a shoreline. In recent years, numerous studies have employed photogrammetric methods, based on different colours, to map the boundary between water and land. These works use images acquired by satellites, drones, or cameras, and differ mainly in terms of resolution. Such methods can identify a shoreline by means of automatic, semi-automatic, or manual procedures. The aim of this work is to find and promote a new and valid beach topography-based algorithm, able to identify the shoreline. We apply the Structure from Motion (SfM) techniques to reconstruct a high-resolution Digital Elevation Model by means of a drone for image acquisition. The algorithm is based on the variation of the topographic beach profile caused by the transition from water to sand. The SfM technique is not efficient when applied to reflecting surfaces like sea water resulting in a very irregular and unnatural profile over the sea. Taking advantage of this fact, the algorithm searches for the point in the space where a beach profile changes from irregular to regular, causing a transition from water to land. The algorithm is promoted by the release of a QGIS v3.x plugin, which allows the easy application and extraction of other shorelines

High-resolution spatial analysis of the temperature influence on the rainfall regime and extreme precipitation in northern-central Italy.

This is the result dataset of the work "High-resolution spatial analysis of the temperature influence on the rainfall regime and extreme precipitation in northern-central Italy" Luppichini et al

Deep learning models to predict flood events in fast-flowing watersheds

This study aims to explore the reliability of flood warning forecasts based on deep learning models, in particular Long-Short Term Memory (LSTM) architecture. We also wish to verify the applicability of flood event predictions for a river with flood events lasting only a few hours, with the aid of hydrometric control stations. This methodology allows for the creation of a system able to identify flood events with acceptable errors within several hours' notice. In terms of errors, the results obtained in this study can be compared to those obtained by using physics-based models for the same study area. These kinds of models use few types of data, unlike physical models that require the estimation of several parameters. However, the deep learning models are data-driven and for this reason they can influence the results obtained. Therefore, we tested the stability of the models by simulating the missing or wrong input data of the model, and this allowed us to achieve excellent results. Indeed, the models were stable even if several data were missing. This method makes it possible to lay the foundations for the future application of these techniques when there is an absence of geological-hydrogeological information preventing physical modeling of the run-off process or in cases of relatively small basins, where the complex system and the unsatisfactory modeling of the phenomenon do not allow a correct application of physical-based models. The forecast of flood events is fundamental for correct and adequate territory management, in particular when significant climatic changes occur. The study area is that of the Arno River (in Tuscany, Italy), which crosses some of the most important cities of central Italy, in terms of population, cultural heritage, and socio-economic activities

High-resolution spatial analysis of the temperature influence on the rainfall regime and extreme precipitation in northern-central Italy.

This is the result dataset of the work "High-resolution spatial analysis of the temperature influence on the rainfall regime and extreme precipitation in northern-central Italy" Luppichini et al.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Statistical relationships between large-scale circulation patterns and local-scale effects: NAO and rainfall regime in a key area of the Mediterranean basin

In this work, we investigated the correlation between the North Atlantic Oscillation (NAO) index and the rainfall trend in Tuscany (Italy) by using a large number of rain gauges for a high-resolution spatial scale study in a region characterized by significant morphological and climatic variability and equipped with an efficient measurement network. The relationship between NAO and rainfall was calculated by the Spearman's correlation coefficient. Our study shows that the correlation between NAO and precipitation has two types of oscillations as functions of the time scale. During the year the correlation is negative in winter and positive in summer, and this is due to global atmospheric circulation, which causes the area to be affected by humid air masses from the Atlantic Ocean during the winter. It is difficult to understand these experimental observations for the summer period because the correlation is influenced by the low rain levels of this area and the results could be easily influenced by other global patterns. Investigating in particular the period from December to March, we observed a variation in the NAO precipitation correlation also over time, with periods characterized by an increase in anti-correlation. The results obtained for Tuscany were contextualized and compared with other areas of Europe and of the Mediterranean basin by applying the same methodology. Spatial analysis showed that the trend of the NAO and rainfall correlation depends on latitude. In northern Europe, the behaviour of the correlation between NAO and rainfall over time is very similar to the temporal trend of NAO itself; instead, in the southern Mediterranean area this correlation has a trend over time that is very similar to the one of the Atlantic Multidecadal Oscillation (AMO) Index. This allowed us to observe a different regulation in the circulation of the humid air masses coming from the Atlantic Ocean, which induced rainfall in the Mediterranean and in northern Europe. The circulation of humid air masses in Northern Europe is linked to the pressure difference between high and low latitudes (represented by the NAO index), whereas in the Mediterranean it is linked to the temperature of the Atlantic Ocean (represented by the AMO index). The areas between high and low latitudes, characterized by a mixed behaviour, are regulated by both pressure difference and ocean temperature. This study has made it possible to investigate a specific area of the Mediterranean and then to extend and contextualize to more geographical locations, highlighting the fact that simple linear regression models can help to investigate the role of global patterns on the local effects

Geomorphological features of Favignana Island (SW Italy)

We present a large scale (1:10,000) geomorphological map of Favignana Island, in which landforms were recognized and genetically categorized based on analysis of stereoscopic aerial photographs and freely available satellite images, complimented by a few validation tests performed in the field. The map was created following the guidelines of the Italian Geomorphological Working Group for landforms symbolic representation. This includes an immediate visual differentiationof the genetic character and the state of activity of landforms. Favignana, belonging to the Egadi Archipelago in the Tyrrhenian Sea (SW Italy), is a mid-sized (ca. 20 km2) mostly carbonatic island. The marine protected area surrounding the island represents the main attraction for tourists. Through this map, it was possible to highlight the richness and diversity, in terms of abundance and peculiarity of landforms, that characterizes the Island. This peculiarity makes of Favignana an excellent candidate for the exploitation of its geoheritage for touristic purposes