Potential evapotranspiration (ET0) is an indicator of great interest for water budget analysis and the agricultural sector. Therefore, the purpose of this study was to make the calculation reliable even if only the temperature data were present. In this research, the ET0 was initially calculated for a limited number of weather stations (12) using the Penman–Monteith method. In some cases, the simplified Penman–Monteith formula was adopted, while in others, as in the case of mountain weather stations, the complete formula was employed to consider the differences in vegetation, deduced from satellite surveys. Subsequently, the ET0 was calculated with the Hargreaves–Samani (HS) formula, calibrating the Hargreaves coefficient, through the spatialization of ET0, by the geostatistical method. The results showed a high reliability of the HS method in comparison with simplified PM (PM) method, and complete Penman–Monteith (cPM) method, with a minimum calibration of the empirical Hargreaves coefficient. In particular, a very good correlation between the results obtained in the mountain environment with the uncalibrated HS method and the cPM method was also observed in this area, while PM showed discordant and much higher results than ET0 compared with the other methods. It follows that this procedure allowed a more accurate estimate of potential evapotranspiration with a view to territory management, both in terms of water resources and the irrigation needs of the vegetation.

The present work contributes to the process of modernization of traditional geomorphological mapping, a fundamental tool for the assessment of the hazard degree of natural processes for the planning, of works and infrastructures. Starting from a traditional and detailed geomorphological survey and through elaborations in a GIS environment, this paper presents a multiscalar cartography model, characterized by a ‘full coverage’ representation of landforms. These characteristics make it possible to upscale or downscale processes and landforms and to use different information levels created in a hierarchical form. The test site for the experimentation is located on the Adriatic side of central Italy and is represented by a small catchment, about 13 km² large. All geomorphological features and information have been organized as elements and attributes within digital geomorphological information layers, following structured on a Digital Terrain Model derived from LiDAR; this new product is here proposed also as web-browser version.

The extreme rainfall events of recent years in central Italy are producing an increase in hydrogeological risk, with disastrous flooding in terms of human lives and economic losses, as well as triggering landslide phenomena in correspondence with these events. A correct prediction of 100-year return levels could encourage better land planning, sizing works correctly according to the expected extreme events and managing emergencies more consciously through real-time alerts. In the recent period, it has been observed that the return levels predicted by the main forecasting methods for extreme rainfall events have turned out to be lower than observed within a few years. In this context, a model widely used in the literature, the generalised extreme value (GEV) with the “block maxima” approach, was used to assess the dependence of this model on the length of the collected precipitation time series and the possible addition of years with extreme events of great intensity. A total of 131 rainfall time series were collected from the Adriatic slope in central Italy comparing two periods: one characterised by 70 years of observations (1951–2020), the other by only 30 years (1991–2020). At the same time, a decision was made to analyse what the effect might be—in terms of the 100-year return level—of introducing an additional extreme event to the 1991–2020 historical series, in this case an event that actually occurred in the area on 15 September 2022. The results obtained were rather surprising, with a clear indication that the values of the 100-year return level calculated by GEV vary according to the length of the historical series examined. In particular, the shorter time series 1991–2020 provided higher return level values than those obtained from the 1951–2020 period; furthermore, the addition of the extreme event of 2022 generated even higher return level values. It follows that, as shown by the extreme precipitation events that have occurred in recent years, it is more appropriate to consider a rather short period because the ongoing climate change does not allow true estimates to be obtained using longer time series, which are preferred in the scientific literature, or possibly questioning the real reliability of the GEV model.

In central Italy, particularly in the Umbria-Marche Apennines, there are some complete, high-altitude weather stations, which are very important for assessing the climate in these areas. The mountain weather stations considered in this study were Monte Bove Sud (1917 m.a.s.l.), Monte Prata (1816 m.a.s.l.) and Pintura di Bolognola (1360 m.a.s.l.). The aim of this research was to compare the differences between the precipitation measured by the rain gauges and the data obtained by satellite using the IMERG algorithm, at the same locations. The evaluation of possible errors in the estimation of precipitation using one method or the other is fundamental for obtaining a reliable estimate of precipitation in mountain environments. The results revealed a strong underestimation of precipitation for the rain gauges at higher altitudes (Monte Bove Sud and Monte Prata) compared to the same pixel sampled by satellite. On the other hand, at lower altitudes, there was a better correlation between the rain gauge value and the IMERG product value. This research, although localised in well-defined locations, could help to assess the problems in rain detection through mountain weather stations.

The present study aims to assess the tectonic activity in the South Setifian allochthonous complex, providing insights into the evolution of the landscape. A morphometric analysis of Jebel Youcef Mountain (JYM) in Eastern Algeria was conducted to assess neotectonic activity. Six quantitative parameters were analyzed: stream length-gradient index, asymmetric factor, hypsometric integral, valley floor width-to-valley height ratio, index of drainage basin shape, and index of mountain front sinuosity across the 16 river basins in the region. The geomorphic indices are combined into a single index of relative tectonic activity (IRTA), categorized into four classes: very high, high, moderate, and low. The results identified two major lineament sets. The NE-SW lineament set is the dominant structural feature, playing a key role in driving recent geological processes and deformation in the study area. In contrast, the E-W and NW-SE lineament sets exert a more localized influence, primarily affecting the Jurassic formations at Kef El Ahmar’s central peak in Jebel Youcef, though they exhibit relatively lower tectonic activity compared to the NE-SW lineament set. Based on the relative active tectonic classes, significant neotectonic activity is evident in the study area, as shown by distinctive basement fracturing. The findings contribute to understanding the structural processes in the study area. Furthermore, the study establishes a systematic framework for analyzing tectonic activity and landscape morphology evolution, enhancing our perception of the convergence between the North African Alpine zones and the Atlas range.