Raffaele Albano

Satellite data provide valuable information on the variations in the surface area of inland waters. These observations are crucial for monitoring remote or poorly instrumented regions and offer complementary insights to in situ measurements. To further assess this capability, we analyzed the temporal variations of Serra del Corvo Lake – located at the border between the Basilicata and Puglia regions in southern Italy – using cloud-free Sentinel-2 imagery acquired from 2015 to 2025. The satellite-derived surface water extent time series were compared with daily in situ measurements of water volume and level, achieving very high correlation (R > 0.98). This strong correlation supported the development of two predictive models, based on both linear and exponential regression method, which demonstrated satisfactory accuracy (R² consistently above 0.8 in both calibration and validation phases). These results highlight the high potential of satellitebased approaches to provide almost independent and reliable information on inland water dynamics.

Climate change is intensifying extreme rainfall events, posing increasing risks to historic cities in vulnerable geomorphological contexts. The city of Matera in Southern Italy, with its unique karstic urban landscape, exemplifies the hydrological challenges associated with surface runoff and flooding. This study developed an integrated methodological framework that combines morphological and hydrological analysis, field data collection, citizen testimonies, hydraulic features estimations using community-sourced observations for extreme rainfall and pluvial flood events reconstruction. The framework was applied to analyse recent urban flood events from 2018 to 2024 that severely affected the heritage “Sassi” district in Matera. Among the identified events, the most significant ones were selected for detailed analysis, allowing the estimation of rainfall recurrence (up to 30–50 years return period), surface flow velocities on the order of 4–6 m/s, and water depths of approximately 0.2–0.4 m, as well as the quantitative assessment of flood hazard based on velocity–depth conditions, together with associated direct damages. The results highlight the inadequacy and/or lack of maintenance of current drainage infrastructure as well as the vulnerability of cultural heritage structures built in porous calcarenite rock. These findings underscore the necessity of upgrading stormwater management and alert systems and embedding community participation in risk management planning and education. The proposed approach offers a replicable framework for post-flood event reconstruction which dataset can support the development of sustainable early warning, alerting and decision support system for increasing community resilience in heritage city.

Current flood early warning and risk communication approaches are often characterized by simple and/or alarmist messages, which can promote non-protective behaviours—either through overreliance on defence structures or emergency management organizations. In response, we propose and develop an early warning system (EWS) prototype aimed at fostering “flood literacy” within communities. This system seeks to empower individuals and local populations to better understand their flood risk by recognizing their personal vulnerability and the characteristics of potential floods affecting them. Such understanding enables timely and appropriate self-protective actions. The proposed EWS comprises an Internet of Things (IoT)-based camera network for monitoring rainfall, water depth, and water velocity based on Artificial Intelligence (AI) techniques. These AI algorithms have been used also to analyze and assess historical flood events in the study area, i.e., the heritage city of Matera (Basilicata Region, Italy). The monitoring system is integrated with AI-driven flood modelling to generate impact scenarios at the local scale. These forecasted scenarios can be compared with historical flood data to contextualize current measurements of rainfall and water levels and therefore the citizens can judge how significant a flood might be. The system incorporates threshold-based alerts related to flood instability for pedestrians, along with signals and symbols designed for quick interpretation and communication of self-protection measures to improve citizen resilience and response.

Urban flood events pose significant risks to pedestrian safety. Analyzing pedestrian stability, during those events, enables the correlation of hazard levels with flood dynamics, which helps to identify critical areas and suggest evacuation routes. The present study introduces a novel approach for determining instability-thresholds for pedestrians during urban flooding. The methodology is based both on instability unit strength and specific energy—two fundamental hydraulic parameters that influence the balance of a body affected by fluid flow actions. The approach employs a physically-based formula that incorporates three calibration factors to balance the static and dynamic components of pedestrian stability, resulting in a conceptual instability threshold curve optimized by fitting a dataset of 417 experimental literature cases. By combining flow velocity and water depth, we identified a graph that identifies three hazard levels. These levels were obtained by minimizing the model’s prediction errors and show areas where pedestrians are at the highest risk of being swept away. The proposed identification of critical thresholds was applied to the urban areas of Matera in the Basilicata Region of South Italy. This case study aimed to demonstrate the method’s effectiveness and its ability to provide valuable insights for flood management. By pinpointing flood-prone roads, warning areas, and safe evacuation routes, this approach supports emergency response efforts, enhances flood preparedness, and ultimately improves public safety during flooding events.

The advent of Cosmic Ray Neutron Sensing (CRNS) stations has represented a significant advancement in the field of soil moisture (SM) retrieval. With footprints of up to 300 m and measurements referring to soil depths of up to 50 cm, these stations can effectively contribute to improving the evaluation of satellite-derived and modelled SM products.Recently, a CRNS station implemented by Finapp S.r.l. was installed in the peri-urban area of Tito (Contrada Carlone – 40.573425 N, 15.676042 E), located in the Basilicata region of southern Italy. The monitoring site, characterized by complex geomorphological conditions, features an integrated setup including time-lapse Electrical Resistivity Tomography (ERT) system, an array of hydrological sensors (tensiometers, soil moisture sensors, piezometers), and meteorological sensors (temperature, humidity, wind speed, and solar radiation). This open-air monitoring laboratory, supported by advanced methodologies for data integration, enables a multidisciplinary and multiscale approach to investigate SM variability and, more broadly, provides insights into the hydrogeological risks affecting the site. The laboratory was established within the framework of the “ITINERIS” project (PNRR M4C2 Inv.3.1 IR), funded by the EU’s Next Generation program.Focusing on the CRNS station, SM data acquired since July 2025 have already been compared with different satellite-based SM products, such as the weekly Copernicus Surface Soil Moisture (SSM) derived from Sentinel-1 SAR data and the daily Soil Water Index (SWI) obtained from ASCAT (Advanced Scatterometer) acquisitions. Additional datasets and products acquired at different temporal and spatial scales, as well as based on diverse technologies (active, passive, and merged), will be considered in future analysis. Preliminary results are promising and highlight the strong potential of the laboratory to produce accurate SM measurements. These measurements will be scaled up to the regional level within the framework of the “Space It Up” project, funded by the Italian Space Agency and the Ministry of University and Research (contract No. 2024-5-E.0 – CUP I53D24000060005), to better investigate the impacts of climate change across the entire region. SM variability as well as spatiotemporal anomalies will be analyzed.

Rapid urbanization and climate change are increasing the frequency and severity of floods, posing significant threats to both lives and infrastructure. To mitigate these risks, it is essential to enhance the alerting and communication mechanisms for pluvial flash floods, thereby improving community resilience and reducing losses.This study proposes an effective, citizen-oriented Early Warning System (EWS) implemented and tested in the heritage city of Matera, Italy. This EWS aims to empower citizens by improving their understanding of local flood risks, enabling them to assess their personal exposure and the potential characteristics of floods that may affect them. This knowledge allows individuals to make informed decisions about when to act and which life-saving measures to take.The system integrates Artificial Intelligence (AI) for flood monitoring, flood modeling, and risk communication. Internet of Thing (IoT)-based cameras combined with deep learning algorithms, specifically the You Only Look Once (YOLO) model, estimate flood water depth and car submergence levels. Additionally, flood surface velocity can be computed using the Fudaa-LSPIV (Large-Scale Particle Image Velocimetry) method. A deep convolutional neural network (CNN) model has been developed for rapid and accurate real-time prediction of water depth and flow velocity of forecasted urban flash flood scenarios. The EWS includes threshold-based alerts concerning flood instability for pedestrians and vehicles, accompanied by signals and designed symbols for communicating risk and self-protection measures to enhance citizen resilience. Overall, the proposed citizen-oriented EWS is not intended to replace existing systems from competent authorities but to complement existing systems by fostering "flood literacy" among citizens. Furthermore, this research can assist municipal authorities in emergency management by providing reliable information about the timing of flood recession, which is crucial for prioritizing the accessibility of affected areas and determining which roads should be restored for traffic in the short term.

Among the data made available by the European Copernicus Sentinel missions, those acquired by sensors aboard Sentinels 1 and 2 satellites have already proven to be suitable for detecting and monitoring floods, with different capabilities depending on the characteristics of the instrument used. C-band SAR aboard Sentinel 1A and 1B can provide all-day and all-weather information with a spatial resolution of 10 m a sub-weekly temporal resolution (when both satellites are working together), whereas the MultiSpectral Instrument on Sentinels 2A and 2B can provide data only in daytime conditions and in the absence of clouds with a slightly lower spatial resolution (i.e., 20 m) and almost a similar temporal resolution. Nevertheless, their integration may allow for a more accurate and comprehensive investigation of the studied event if adequate data analysis methodologies are used. This study presents a multitemporal approach to map flooded areas using long-term historical series of Sentinel 1 and Sentinel 2 data. Such an approach, based on a preliminary characterization of the expected value of the investigated signal at the pixel level, can allow for robust identification of any signal transients related to the occurrence of statistically significant change within the pixel, such as a different and/or increased water presence due to floods. The Google Earth Engine (GEE) cloud computing system, where all historical data are present and accessible, was used to implement the proposed methodology. In addition to Sentinel 1 and 2 data, all other datasets/tools useful for developing the proposed methodology are already available in GEE, facilitating its implementation and application to the analysis of different flood episodes. The results achieved for a few events that occurred worldwide using each single approach and their integration were compared with flood maps made available by the Copernicus Emergency Monitoring Service system to assess their accuracy. The performance achieved is discussed in this study.