Coastal landscapes are one of the most changeable areas of the earth’s surface. Given this spatial complexity and temporal variability, the construction of reference maps useful for geo-engineering is a challenge. In order to improve the performance of geomorphic models, reliable multiscale and multi-temporal base maps and Digital Elevation Models (DEM) are needed. The work presented in this paper addresses this issue using an inter-geo-disciplinary approach to optimize the processing of multisource and multi-temporal data and DEMs by using field surveys, conceptual model, and analytical computation on a test area. The data acquired with two surveying techniques were analyzed and compared: Aerial Laser Scanning (ALS) and photogrammetry from stereo pairs of High-Resolution Satellite Images (HRSI). To assess the reliability of the DEMs produced from point clouds, the residuals between the point cloud and the interpolated filtered surface were identified and analyzed statistically. In addition to the contour maps, some feature maps such as slope, planar, and profile curvature maps were produced and analyzed. The frequency distribution of the slope and curvature values were compared with the diffusion, advection, and stream power model, revealing a good agreement with the past and present geomorphic processes acting on the different parts of the study area. Moreover, the integrated geomatics–geomorphic analysis of the outliers’ map showed a good correspondence (more than 75%) between the identified outliers and some specific geomorphological features, such as micro-landforms, which are significant for erosive and gravity-driven mechanisms. The different distribution of the above singularities by different data sources allowed us to attribute their spatial model to the temporal variation of the topography and, consequently, to the geomorphic changes, rather than to the different accuracy. For monitoring purposes and risk mitigation activities, the methodology adopted seems to meet the requirements to make a digital mapping of the coast analyzed, characterized by a rapid evolution of the surface, and can be extended to other stretches of coast with similar characteristics.

A model has been developed for the calculation of the Radon activity concentration in indoor air, taking into account all the different important parameters responsible of the global Radon dynamics, from its generation into the source (soil, water or building material) to its accumulation in confined spaces. The Radon accumulation into buildings is mainly due to the transport from the soil dominated by diffusion, pressure driven flow or a mixture of both depending on the real conditions. Experimental observations and measurements were carried out on different sites in the Campania Region (Italy) to gain valuable information about the various parameters influencing the indoor Radon level. The acquired results indicate that, generally, in the regional area, indoor Radon accumulation is not only due to the transport from the ground to the building, driven by pressure differences, through the basement, but it is also due to the direct exhalation from building materials. So, aim of this work was to refine a physical mathematical model of the Radon dynamics, developed in a previous work by the authors, by gathering and incorporating realistic experimental values. A graphical plot has been elaborated for an easy visualization and interpretation of the data.

The aim of the paper is to apply an object-based geomorphometric procedure to define the runoff contribution areas and support a hydro-geomorphological analysis of a 3 km² Mediterranean research catchment (southern Italy). Daily and sub-hourly discharge and electrical conductivity data were collected and recorded during a 3-year monitoring activity. Hydro-chemograph analyses carried out on these data revealed a strong seasonal hydrological response in the catchment that differed from the stormflow events that occur in the wet periods and in dry periods. This analysis enabled us to define the hydro-chemograph signatures related to increasing flood magnitude, which progressively involves various runoff components (baseflow, subsurface flow and surficial flow) and an increasing contributing area to discharge. Field surveys and water table/discharge measurements carried out during a selected storm event enabled us to identify and map specific runoff source areas with homogeneous geomorphological units previously defined as hydro-geomorphotypes (spring points, diffuse seepage along the main channel, seepage along the riparian corridors, diffuse outflow from hillslope taluses and concentrate sapping from colluvial hollows). Following the procedures previously proposed and used by authors for object-based geomorphological mapping, a hydro-geomorphologically oriented segmentation and classification was performed with the eCognition (Trimble, Inc.) package. The best agreement with the expert-based geomorphological mapping was obtained with weighted plan curvature at different-sized windows. By combining the hydro-chemical analysis and object-based hydro-geomorphotype map, the variability of the contribution areas was graphically modeled for the selected event, which occurred during the wet season, by using the log values of flow accumulation that better fit the contribution areas. The results allow us to identify the runoff component on hydro-chemographs for each time step and calculate a specific discharge contribution from each hydro-geomorphotype. This kind of approach could be useful when applied to similar, rainfall-dominated, forested and no-karst catchments in the Mediterranean eco-region.

Understanding of runoff generation mechanisms affects the ability to manage streamflow quantity and quality issues. Concerning the baseflow in particular, measurements are almost never available and hydrograph separation is generally applied to characterize its relevant patterns. As an alternative to well-known recursive digital filters and mass balance filtering methods, this paper deals with the use of regression approaches, based on electrical conductivity measurements, as a proxy for total dissolved solids, to separate baseflow from total flow. Particular focus is placed on their flexibility and ability to adapt to discontinuous electrical conductivity data measurements. To illustrate this, we analyze a hydrochemical dataset collected from the Ciciriello experimental catchment (Southern Italy). The main findings are as follows: A comparative analysis suggests that the performance of regressive approaches in the case of daily electrical conductivity measurements is better than that of calibrated recursive digital filters. Weekly monitored electrical conductivity data led to performances comparable to the daily scale monitoring, and even monthly observation leads to a nonsignificant reduction in regression hydrograph filter performance; this shows how spot geochemical data monitoring may present valid and operational alternatives for characterization of baseflow in poorly gauged catchments.