The combined use of Unmanned Aerial Vehicles (UAVs) and Structure from Motion (SfM) photogrammetry allows the collection and processing of high-resolution data on demand, which is key for the constant and detailed monitoring of the fluvial environment. In addition, through the ever-increasing development of new techniques of mapping and data processing (i.e., UAVs swarm, BVLOS flight missions, high-performance photogrammetry workflow), it is now possible to detect large areas at high-resolution, providing support for spatiotemporal insights into hydro-geomorphological processes and hazard assessment, in order to ensure an effective management and to prevent catastrophic phenomena (i.e., floods). The main goal of this paper is to use UAV-based high-resolution topographic data to constrain the probabilistic hazard assessment of extended reaches of Basento river (Basilicata, Italy). To demonstrate the influence of geomorphology and riverbed sediment on hazard assessment, a sensitivity analysis was carried out on the resolution of the UAV-derived DEMs; on the riverbed roughness coefficient resulting from photo-sieving analysis and on the morphological change detection over short-time ranges (2019-2021). We found that lower resolution DEMs lead to an increase of flooding probability (in several river cross-sections an increase even higher than 99 % resulted), and that a greater roughness coefficient involves an increase in the probability of flooding (with a maximum increase of about 9 %). Moreover, the multitemporal high-resolution outputs resulting from SfM allowed the identification of morphological changes, in short-times, caused by an anthropic modification of the river bank, which significantly affected the flooding hazard.

Digital elevation models (DEMs) represent a fundamental resource in geomorphological analysis. The increasing availability of open‐access DEMs over wide areas is advantageous, but requires an evaluation of DEM quality and errors. This work applies a hierarchical assessment of global, continental and national DEMs in Italy in order to explore the differences and analyze the vertical accuracy, spatial error distribution and agreement of morphometric measurements. The selected DEMs are compared with local reference data as a ground points dataset, extracted from the national geodetic network, and regional DEMs at high spatial resolution, through both a qualitative and a quantitative approach. The results identify limits and potentialities of the selected DEMs, showing accuracy and errors in height representation, also affected by the topographic characteristics of the surface, such as steep slope in mountain zones and some defects in hydromorphological derivatives that could condition the geomorphological applications.

Over the past two decades, natural hazards have claimed the lives of tens of thousands of people worldwide, every year. Unmanned Aerial Vehicles (UAVs) are pivotal in natural hazard management, offering rapid deployment, flexibility, and cost-effectiveness. Advances such as Beyond Visual Line Of Sight (BVLOS) missions, swarm surveying, Artificial Intelligence (AI), edge-computing, and Structure from Motion (SfM) photogrammetry enhance their high-resolution spatiotemporal data capabilities, but the need for large datasets poses challenges in terms of storage, computational resources and, especially, processing time. This work introduces an original high-performance UAV photogrammetry workflow through the implementation of an open-source distributed approach using the ReCaS-Bari HPC cluster. Performance tests of the workflow, that includes computing parallelism, GPU usage, and hybrid bundle adjustment, demonstrate a significant reduction in processing time for large UAV image datasets. The workflow outperformed current methods, reducing processing time from 908 down to 104 min for 2,691 images and handling 11,549 images in just 7.8 h (a 70 % improvement over leading commercial software). Comparative analysis with cluster-based state-of-the-art approaches revealed noteworthy reductions, reaching up to 86 % for about 7,000 images. A case study, focusing on the Basento river (Southern Italy) flood event occurred in May 2023, proved the workflow practical implications in emergency management. A change detection assessment facilitated the identification and quantification of flood-induced morphological alterations along a 3 km of river reach length within about 3 h. The results highlight the workflow utility in providing accurate and near real-time information for emergency management, enhancing situational awareness and facilitating informed decision-making during disastrous events.

The integration of geomorphological analysis in archaeological investigations is essential to describe physical geography and land morphology in order to understand the relationship between the environment and human activities. Recently, the sediment flow connectivity index (SfCI) has been demonstrated to be a powerful geomorphic indicator for defining the most sensitive areas to geomorphological modifications in a catchment. This work presents the experimental application of the SfCI for a landscape archaeological analysis in order to assess the contribution of the index to potentially recognize, monitor, and interpret the historical evidence in the evaluation of landscape evolution. The investigation was performed in the basin of Lama Camaggi in the Apulia region (southern Italy), characterized by precious archaeological evidence found on the surface during field surveys in the years 2001–2002 and 2012–2013. The results show (1) the correlation between high-sediment-connectivity areas and areas with high densities of archaeological sites, and (2) the capacity of the SfCI to identify surface processes that may potentially affect the readability of the archaeological records to support data interpretation. These results confirm the advantage of applying an interdisciplinary approach in archaeology and opens innovative research scenarios.

Reliable rainfall information is fundamental for climate-risk analysis and operational monitoring in Mediterranean regions such as Apulia (Southern Italy), one of the areas most affected by climate change-driven shifts in rainfall patterns. Recent evaluations across Italy and comparable Mediterranean settings consistently show that gridded precipitation performance is highly dependent on orography and dataset typology: reanalyses often provide the best overall agreement with gauges, while satellite and blended products can exhibit larger biases, with persistent challenges in complex terrain and for high-intensity events. In this context—and given the documented spatial heterogeneity of rainfall extremes within Apulia—validation of such gridded datasets with respect to ground observations remains essential for early warning and climatological applications. In the present work, we evaluate four widely used precipitation products—CHIRPS-v2, the newly released CHIRPS-v3, IMERG, and ERA5—benchmarking them against the Apulia region Civil Protection rain-gauge network. We provide diagnostics aligned with early warning and climate monitoring: bias and error statistics, rainfall intensity distributions, and dry spell duration. A key contribution is, to our knowledge, the first dedicated validation of CHIRPS-v3 in Apulia, which is timely given that CHIRPS-v3 was explicitly developed to address shortcomings such as underestimated temporal variance and to leverage expanded station inputs. The results indicate that CHIRPS-v3 yields systematic improvements over CHIRPS-v2 across multiple metrics, while ERA5 generally shows the strongest overall agreement with gauges—consistent with broader Italian evidence.

A lot of different methods are used to estimate flood risk worldwide. The method that performs better depends on the catchment features and dimension, data time resolution and availability and uncertainty level required. Remote sensing approaches are more and more common, but because of the limited periods covered by time series derived by this new methodology, in-situ data integration is still required. A new methodology is proposed, based on a case-study of different reaches of Basento river, Basilicata (Southern Italy). Starting from hourly rainfall time series (covering not less than 20 years), for each pluviometric station taken into account into the catchment area, Intensity-Duration-Frequency (IDF) curves are computed (fitting a power law), in order to calculate the rainfall maximum at a certain percentile (typically 90° or 95° percentile are used) during the concentration time. Thiessen polygon method is used to divide the catchment area into smaller areas, each one corresponding to a pluviometric station, with the purpose of calculating weighted  rainfall values for each station area. A Digital Terrain Model is used to extract multiple cross sections of the river-bed, spanning different morphologies, from braided to meandering channels. For each cross section, starting from bankful level, it is possible to estimate diverse hydraulic parameters such as river stage, hydraulic radius, section’s surface area (using image analysis) and the mean velocity of the current, using the logarithmic law profile of the turbulent flow. Sediment size analysis is carried out as to estimate the river bed roughness for each cross section. The mean velocity value V can be used to estimate the concentration time t=L/V, where L is equal to the distance between the cross section and the hydraulically further point into the catchment area. The concentration time value t is used into the equation of the IDF curves, in order to link the corresponding rainfall height to the river stage reached at the cross section, eventually to estimate the rainfall value that, if exceeded, can cause flood. A FLO-2D model has been then used to run simulations with the aim to detect flood-prone areas, finding an overall good matching between the values of current mean velocity, discharge and river stage estimated in the cross sections.