Floods and landslides are two distinct natural phenomena influenced by different conditioning factors, though some environmental triggers may overlap. This study applied eXtreme Gradient Boosting (XGBoost) to develop susceptibility maps for both phenomena, using a unified approach based on the same geospatial predictors. The approach integrated topographical, geological, and remote sensing datasets. Flood event data were collected from institutional sources using multi-source and high-resolution remotely sensed data. The landslide inventory was compiled based on historical records and geomorphological analysis. Key conditioning factors such as elevation, slope, lithology, and land cover were analyzed to identify areas prone to floods and landslides. The methodology was applied to the Basento River basin in Southern Italy, a region frequently impacted by both hazards, to assess its vulnerability and inform risk management strategies. While flood susceptibility is primarily associated with low-lying areas near river networks, landslides are more influenced by steep slopes and geological instability. The XGBoost model achieved a classification accuracy close to 1 for flood-prone areas and 0.92 for landslide-prone areas. Results showed that flood susceptibility was primarily associated with low Elevation and Relative Elevation, and high Drainage Density, whereas landslide susceptibility was more influenced by a broader and balanced set of factors, including Elevation, Drainage Density, Relative Elevation, Distance and Lithology. The resulting susceptibility maps offered critical approaches for land use planning, emergency management, and risk mitigation. Overall, the results demonstrated the effectiveness of XGBoost in multi-hazard assessments, offering a scalable and transferable approach for similar at-risk regions worldwide.

Spur dikes are elongated structures extending from banks into rivers that mitigate erosion by forcing the flow away from the bank. The research on grouped spur dikes is insufficient in comparison with those on isolated spur dikes. Most of the studies focus on the maximum scour depth, omitting the bed morphological changes induced by local scour processes. Moreover, as yet, there is no established procedure for predicting the scour depth around spur dikes. This study aims to provide insights into the temporal and spatial morphological patterns around a single spur dike and spur dikes in cascade (three, five, and seven, consecutively). Experiments (of up to 318 h) were performed on a rectangular straight channel with dimensions of 20 m (length) × 1.0 m (width) × 1.0 m (depth). Nearly uniform sand with median grain size of 1.7 mm and sediment gradation of 1.5 was used for the mobile bed. The spacing between the elements for the spur dikes in cascade was 3b, where b is the spur dike width. All runs were conducted under a clear-water regime and steady flow conditions. Some limitations of the formulas for the equilibrium scour depth at the first spur dike reported in the literature were emphasised, with underestimations up to 160% and overestimations up to 200% at the earlier scour stages. The temporal evolution of the scour depth at the first spur dike was satisfactorily predicted with a correlation coefficient of 0.91. The scour processes at the other spur dikes were delayed and started at a dimensionless time greater than approximately 10³. However, the scour rates increased to a high degree, with the scour depths tending to match those at the first spur dike.

Flood damage assessment remains challenging, as conventional flood risk management mainly relies on hydraulic hazard maps that do not explicitly reproduce observed damage patterns. Recent advances in remote sensing and machine learning (ML) enable the integration of environmental and socio-economic data with historical impact information to improve flood damage modeling. This study proposes an explainable machine learning framework for flood damage susceptibility mapping, using observed institutional damage records from the 2011 and 2013 flood events combined with 17 geospatial flood risk factors (FRFs) representing hazard, exposure, and vulnerability. This approach enables the capture of non-linear relationships between flood damage and FRFs. For comparison purposes, the same framework was also applied using hydraulically modeled flood extents corresponding to return periods of 30, 200, and 500 years. The framework was tested along the Basilicata Ionian coast in southern Italy, a Mediterranean region characterized by complex geomorphology, intense rainfall events, and recurrent flood impacts. An eXtreme Gradient Boosting (XGBoost) model was trained using 17 FRFs related to hazard, exposure, and vulnerability at a spatial resolution of 20 m. The model achieved high performance with an accuracy of 0.988, an F1-score for the minority class of 0.860, and an ROC-AUC (test) of 0.996. High to very high flood damage probability was predicted in approximately 4.1% of the study area, mainly in low-lying floodplains near river corridors and infrastructure. SHAP-based explainability analysis revealed that damage susceptibility was predominantly driven by hazard and exposure factors: Drainage density (17.10%), Railway distance (16.33%), and Elevation (15.42%), extreme precipitation (Max rainfall, 10.66%) and Street distance (7.51%), with socio-economic vulnerability contributing less than 4%. The observed damage target exhibited clear threshold-like patterns (e.g., sharp risk increases below ~25/35 m elevation or within ~150/200 m of road infrastructure), contrasting with the smoother, continuous gradients produced by hydraulic scenarios. This analysis identified the most influential predictors and their response ranges. The proposed framework complements hydraulic hazard mapping by explicitly modeling observed flood damage, supporting flood risk assessment in flood-prone coastal regions.

Hydrological observations are essential for understanding the complex interactions between the land surface and the atmosphere, improving water resource management, strengthening flood defense, and advancing hydrological modeling. However, the long-term maintenance of experimental basins like Fiumarella di Corleto presents significant challenges, requiring continuous updates to address environmental changes and technological advancements. This study reviews over 20 years of observations at the Fiumarella basin in Southern Italy, focusing on its evolution, challenges, and future directions. The Fiumarella basin, covering an area of 32.5 km², includes a sub-basin of 0.65 km². Since 2002, a hydrometeorological network has been monitoring key variables such as rainfall, temperature, wind, and streamflow, capturing hydrological variability across spatial and temporal scales. In 2006, 22 soil moisture probes were installed along a 60-meter transect at depths of 30 and 60 cm. Additionally, high-resolution LiDAR data and pedological studies have enhanced the understanding of the basin’s morphology and soil characteristics. The maintenance of this experimental basin has posed substantial challenges. Frequent extreme flood events have resulted in significant damage to hydrometric stations, requiring reconstruction and recalibration. Moreover, the sediment and debris accumulation in the retention basin of the sub-basin necessitated periodic clearing to maintain functionality and ensure continuous data collection. These challenges underscore the effort and adaptability required to sustain long-term monitoring in dynamic environments. Data collected from the basin have significantly contributed to hydrological science. Analyses of peak flow events and antecedent soil moisture conditions have provided insights into flood response mechanisms. Spatial and temporal variability in hydrological processes has informed the calibration and validation of semi-distributed hydrological models, enhancing their accuracy and reliability. These findings highlight the importance of integrating diverse datasets such as soil moisture, precipitation, topography, and land use—for comprehensive hydrological research. Looking ahead, planned upgrades aim to further enhance the basin’s capabilities. The installation of a meteorological radar would improve rainfall measurement precision and expand spatial coverage, thereby addressing existing data gaps. Additional hydrometric sensors and automated systems would increase the granularity and reliability of observations, supporting high-resolution analyses. These advancements will ensure that the Fiumarella basin remains a state-of-the-art research facility capable of addressing emerging challenges in hydrology and climate science.This abstract is part of the project NODES which has received funding from the MUR-M4C2 1.5 of PNRR funded by the European Union - NextGenerationEU (Grant agreement no. ECS00000036).The present research has been carried out within the RETURN Extended Partnership and received funding from the European Union Next-Generation EU (National Recovery and Resilience Plan -NRRP, Mission 4, Component 2, Investment 1.3 - D.D. 1243 2/8/2022, PE0000005).