Irrigation is a significant component of the water cycle in agricultural regions, but information about how much and where irrigation is applied is scarce and uncertain. Remote sensing-based products have been developed to retrieve irrigation across scales, but they are limited by spatial uncertainties and confounding factors, particularly in highly fragmented agricultural landscapes. This paper presents a novel method to estimate irrigation at the field scale by integrating Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter data and Sentinel-2 NDVI data within a modeling framework. The proposed approach involves jointly calibrating the Water Cloud Model (WCM) and a two-layer Soil Water Balance (SWB) model to simultaneously simulate backscatter, irrigation, surface and root-zone soil moisture, and other water balance components. The model is driven by reanalysis meteorological data, and selected parameters in the WCM and SWB are calibrated exclusively using Sentinel-1 backscatter data, thus making the framework independent of in situ ground measurements. The model is tested on 38 fields within an irrigation district in the Po Valley, Italy, during 2018, for which irrigation data are available as reference. It is shown that the capability to estimate irrigation events depends on the treatment of backscatter and the choice of the calibration period. Average time series correlations of 0.43 to 0.95 are obtained for daily to bi-weekly irrigation estimates, respectively, with an associated bias amounting to 0.15 mm/day and 2.25 mm/15 days, and around 25 mm for annual estimates.

This study presents an evaluation of the Surface Water and Ocean Topography (SWOT) mission for monitoring riverine hydrodynamics, using the Po River (Northern Italy) as a test case. Given the recent launch of SWOT, its application to river hydraulics remains relatively unexplored and requires thorough validation against in-situ observations and specially, physically based models. We assess SWOT Water Surface Elevation (WSE), water surface slope, and recently released discharge products during the science phase, by comparing them with gauge measurements and hydrodynamic simulations over a ~300 km reach of the Po River, including the delta section (a coupled 1D/2D HEC-RAS model is employed to dynamically simulate river hydraulics).The analysis integrates multiple SWOT products, including the Level-2 High-Rate Pixel Cloud (SWOT_L2_HR_PIXC) and Level-2 River Single-Pass Vector Product (SWOT_L2_HR_RiverSP - RiverSP), explicitly accounting for quality flags and performance under different flow regimes.Results highlight the critical importance of quality-aware filtering for reliable use of SWOT observations. Good- and degraded-flagged WSE data generally show strong agreement with both in-situ measurements and model simulations, whereas suspect and bad flagged observations exhibit significantly larger discrepancies, with deviations reaching several meters. The analysis also reveals spatial patterns in SWOT performance linked to geophysical and orbital factors.Comparison of RiverSP WSE node data against 10 in-situ stations shows biases up to ~10 cm for good and degraded data, with mean Kling–Gupta Efficiency (KGE) values of 0.92 and 0.82 for high-flow and low-flow regimes, respectively. Across all 114 analysed SWOT passes (each representing a longitudinal river WSE profile), on average, 68% of node observations per pass, are flagged as good or degraded, while 12 passes contain no usable data. Nevertheless, approximately 82% of the profiles show high agreement with the hydrodynamic model (KGE ≥ 0.8), highlighting the strong performance of SWOT in reproducing river WSE profiles. Profile-based comparisons also reveal orbit-dependent performance variability among different SWOT passes.To address data limitations and improve spatial coverage, complementary Pixel Cloud products are leveraged for their higher spatial resolution, although these require extensive preprocessing, including spatial filtering and noise/outlier removal.The study further explores the spatial and temporal performance of SWOT observations in relation to (i) distance from nadir track, (ii) satellite pass orientation, (iii) river planform geometry (e.g. straight vs. meandering reaches), and (iv) flow regime (e.g. rising limb, peak, recession, low flow). Although based on a single case study, the results illustrate both the potential and current limitations of SWOT products for riverine applications. The findings emphasize the importance of integrating quality-controlled satellite observations with physically based hydrodynamic models to support operational hydrology, long-term monitoring, and decision-making for flood and drought risk mitigation in inland-to-coastal environments. The proposed methodology is readily transferable to other river systems for inter-basin comparative analyses under diverse hydraulic conditions.

Floods pose significant threats to railway infrastructure, given their linear extension across diverse landscapes and frequent intersections with rivers. While European countries have developed Flood Risk Management Plans (FRMPs) following the EU Floods Directive, a comprehensive analysis of railway network flood susceptibility at national scale is still lacking for Italy. Here we develop a comprehensive flood hazard classification for the Italian Railway Network (IRN). Our methodology integrates flood hazard maps, railway infrastructure data, and digital elevation models to characterize flood hazard classes along flood-prone railway routes. The approach distinguishes between steep rapid, rapid and slow flood processes, based on topographical characteristics. Results demonstrate that, for the low probability flood hazard scenario (return period >= 500 years), 25.63 % of the IRN (4,523.4 km) exhibits flood susceptibility, with this proportion declining to 19.09 % and 9.77 % for medium and high flood hazard, respectively. By performing a regional analysis across seven hydrographic districts in Italy, a substantial spatial variability emerges, with the Po River district encompassing nearly half (47.5 %) of all flood-prone railway sections. Our analysis reveals also a marked predominance of rapid flood processes, characterized by values for the time of concentration <12 h. Our classification framework provides crucial insights for risk mitigation and resource allocation, relying exclusively on FRMPs and digital elevation models. The methodology presents a scalable approach applicable to other transportation networks and study areas, supporting infrastructure managers in developing targeted flood protection measures.

Flood-induced technological accidents (NaTech events) can release hazardous substances into riverine and floodplain environments, posing risks to ecosystems, water resources, and human health. Tools specifically designed to simulate oil dispersion during floods remain scarce and are often adapted from computationally demanding marine models. This study presents a reduced-complexity two-dimensional oil spill module integrated into the open-source CAESAR–LISFLOOD framework. The model was benchmarked against TELEMAC-2D using a synthetic channel and a real flood-prone river system in Italy. Results show that CAESAR-LISFLOOD reproduces oil dispersion patterns and contaminated areas with good agreement. Computational performance improved substantially, with runtime reductions of 60–85% depending on domain complexity and resolution. The proposed framework combines physical reliability, computational efficiency, and open-source accessibility, making it suitable for rapid-response applications, scenario analysis, and NaTech risk assessment, while providing a flexible basis for future implementation of additional oil weathering processes.

Managing flood risk is crucial for achieving global sustainability. Flood damage to firms' assets, in particular, imposes significant financial stress, necessitating efforts to minimize future consequences. However, current tools and knowledge for estimating flood damage to firms are inadequate, primarily due to a lack of high-quality damage data and the diversity of firm characteristics, complicating generalization. This study aims to improve understanding of micro-scale flood damage to firms in Italy through the analysis of empirical data, focusing specifically on direct damage. The dataset comprises 812 observed damage records collected after five flood events. Damage is categorized into building structure, stock, and equipment. The analysis reveals relationships between damage, economic sector, and water depth. Results indicate that damage increases at a rate less than proportional to the firm surface area and with water depth significantly explaining only stock damage. The quantification of damages across different sectors shows that healthcare facilities register the highest average damage to building structures, the commercial sector is most affected in terms of stock damage, and the manufacturing sector exhibits the greatest average damage to equipment. The derived damage model offers better predictive accuracy than foreign models in the Italian context. These findings aid in developing effective, tailored risk mitigation strategies and provide valuable insights for future research and policy aimed at reducing flood impacts on firms in Italy.

The interaction between rivers and coastal water bodies is critical to hydrological and ecological systems, particularly under the accelerating impacts of climate change. This study investigates the hydrodynamics of the Po river and its interactions with the Adriatic Sea during extreme events such as backwater effects during floods and saline intrusion during droughts. Using high-resolution data from Surface Water and Ocean Topography (SWOT) mission, integrated with in situ measurements, detailed LiDAR datasets, and a hybrid 1D-2D modeling approach in HEC-RAS, the research advances understanding of river-coast dynamics and their responses to climate-induced pressures.The SWOT satellite, launched in December 2022, employs cutting-edge Ka-band Radar Interferometry (KaRIn) technology. The mission provides a variety of hydrological products for the surface water dynamics, with a revisit cycle of 21 days. For the inland rivers, the products include high-accuracy observations of water surface elevation, width, and slope, over a 120 km swath, allowing for improved rating curves and flow duration analysis. Stretching over 650 kilometers and flowing through eight Italian regions, the Po river is a lifeline for the northern region.HEC-RAS is used to simulate riverine and floodplain dynamics, combining the computational efficiency of 1D modeling for long river reaches with the spatial detail of 2D modeling in areas with complex flow patterns, such as floodplains and river-coast interfaces. LiDAR-derived digital elevation models (DEMs) provide the foundation for defining cross-sectional profiles and updating hydraulic geometry, enabling precise representation of terrain and channel morphology.The research follows a multi-phase methodology: SWOT data are processed to derive water surface elevations and extents, validated using in situ measurements and compared with HEC-RAS simulations. The study emphasizes extreme conditions, quantifying backwater effects during high flows and the severity of saline intrusion under low-flow scenarios. The integration of SWOT data with the HEC-RAS model allows for a detailed analysis of hydrodynamic processes, supporting the development of risk prediction models and improving water resource management strategies.

Flooding has become an escalating threat over the past years, driven by climate, land use and socio-economic changes. In Europe, floods now surpass other natural disasters in severity, causing substantial economic losses, particularly in the companies (commercial and industrial sectors). While river flood impacts on agriculture and residential properties have been extensively studied, research on companies losses remain limited despite their significantly high direct damages. This study enhances fluvial flood risk assessment for company assets by integrating flood hazard scenarios with flexible state-of-the-art Bayesian Network-based flood loss model and object-specific exposure data. It estimates the expected annual damage (EAD) to company properties across Europe under a baseline and potential future scenarios shaped by climate change, exposure dynamics, and their combined effects. Additionally, the study assesses the potential of property-level precautionary measures to mitigate flood risks. Results indicate that, compared to the baseline (year-1995), the EAD values could rise more than 5-fold under RCP4.5 scenario and 7-fold under RCP8.5 scenario by the end of the century. However, a policy scenario in which all companies implement at least one precautionary measure ("measures for all") effectively offsets these projected losses by up to 67%. This underscores the crucial role of individual actions in reducing future flood impacts.

Knowledge of river bathymetry is crucial for accurately simulating river flows and floodplain inundation. However, field data are scarce, and the depth and shape of the river channels cannot be systematically observed via remote sensing. Therefore, an efficient methodology is necessary to define effective river bathymetry. This research reconstructs the bathymetry from existing global digital elevation models (DEMs) and water surface elevation observations with minimum human intervention. The methodology can be considered a 1D geometric inverse problem, and it can potentially be used in gauged or ungauged basins worldwide. Nine global DEMs and two sources of water surface elevation (in situ and remotely sensed) were analyzed across two study areas. Results highlighted the importance of preprocessing cross-sections to align with water surface elevations, significantly improving discharge estimates. Among the techniques tested, one that combines the slope-break concept with the principles of mass conservation consistently provided robust discharge estimates for the different DEMs, achieving good performance in both study areas. Copernicus and FABDEM emerged as the most reliable DEMs for accurately representing river geometry. Overall, the proposed methodology offers a scalable and efficient solution for cross-section reconstruction, supporting global hydraulic modeling in data-scarce regions.

The deterioration of superficial water quality is a relevant issue worldwide and most European rivers do not achieve the qualitative standards required by the Water Framework Directive (WFD). Furthermore, the ecological status is defined referring to surveyed data, which is available only along main watercourses and often appears erratic in time and space. Given the goals of the WFD, a short-cut methodology to perform the assessment of water pressures on rivers starting from easily accessible data is proposed. The methodology relies on machine learning techniques and implements a procedure to: (1) identify river segment exposed to pollution spills with a raster-based numerical model; (2) introduce and estimate the spatial allocation of a Biochemical Quality Index (BQI) for each exposed river segment. The study proposes a predictive tool to assess the water quality status using a machine learning algorithm trained starting from easily available input data, such as climatic and hydrological variables, anthropic pressures, water management techniques. In this prospective, the BQI is used as a reliable proxy variable to represent the anthropogenic pressures that impacts on superficial water bodies. Results show that the BQI is well reflected in the monitoring values of COD, used as proxy variable for the quality status of watercourses. We argue that the methodology can represent a solid tool for decision-making processes and predictive studies in areas with no, or poor, monitoring data.

The Surface Water and Ocean Topography (SWOT) satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Discharge is estimated from SWOT observations of water surface elevation, width, and slope. A first assessment using just the highest quality SWOT measurements, over the first 15 months (March 2023–July 2024) of the mission evaluated at 65 gauged reaches shows results consistent with pre‐launch expectations. SWOT estimates track discharge dynamics without relying on any gauge information: median correlation is 0.73, with a correlation interquartile range of 0.51–0.89. SWOT estimates capture discharge magnitude correctly in some cases but are biased (median bias is 50%) in others. There are already a total of 11,274 ungauged global locations with highest quality SWOT measurements where SWOT discharge is expected to accurately track discharge variations: this value will increase as SWOT data record length grows, algorithms are refined and SWOT measurements are reprocessed. This first look indicates that SWOT discharge is performing as expected for SWOT data that achieve performance requirements, providing observed information on discharge variations in ungauged basins globally.

Increasingly frequent and intense flood events, combined with the remarkable industrialization process of cities, are placing transportation networks under stress. The loads that roads and railways must resist are nowadays often greater than those considered for their design; furthermore, their state of ageing is such that any disturbance (flood, earthquake, landslide) could cause a total or partial interruption of traffic resulting in socio-economic losses.Bridges represent the most vulnerable component of a transport system and their failure can compromise the functionality of the entire network, as well as causing loss of life. During floods, bridges can be partially or completely submerged, having to withstand higher hydrodynamic loads which can lead to the collapse of the structure itself. Furthermore, accumulations of large wood and scour total around the bridge piers can reduce the load-bearing capacity of the structure and therefore its structural integrity.In this study, we investigated the hydrodynamic actions and the 3-dimensional flow field at a model bridge (comprising deck and pier) using CFD (Computational Fluid Dynamics) modelling. Drag and lift forces acting on the rectangular-shaped deck were estimated for different submergence values to evaluate the structure's maximum permissible load. In particular, drag and lift coefficients were calculated by simulating various flow conditions (Froude number varying between 0.16 and 0.50) and adopting three different turbulence models (RNG, k-ε, k-ω).In addition, the effect on the drag coefficient of the accumulation of large wood around the pier was also examined, considering different geometries. Numerical simulations, performed for both fixed and live river bed conditions, were validated using experimental data. However, the trends of the synthetic curves constructed so far have presented characteristics similar to those present in the literature, with all positive values ​​for the drag coefficient and negative ​​for the lift coefficient. The emerging evaluations allow us to provide useful indications to designers to evaluate the possible state of stresses on existing bridges and improve knowledge for designing new ones.

Floods are the most common natural disaster, and recent studies suggests that their frequency and magnitude will increase due to climate change. Factors such as demographic growth, urbanization, and land consumption contribute to heightened vulnerability for structures, infrastructure, and populations, elevating the risk of cascading incidents. In this context, flood events can lead to multiple simultaneous releases of hazardous materials, causing severe harm to both the environment and human health. In these cases, the term Natech accident is used, referring to industrial accidents triggered by natural events, for which a multi-risk approach is required. Natech accidents caused by floods require particular attention, as the high velocities of water can rapidly transport pollutants to areas far from the point of emission. The need to focus on this issue is further justified by the fact that many chemical and petrochemical plants are in flood-prone areas, making them particularly vulnerable to the risk of failure following a flood. In the context of emergency management, having access to rapid-response models for assessing the fate and transport of spills is crucial for evaluating their trajectory and for planning recovery interventions. Additionally, these models are key for generating risk maps for various spill scenarios. Within Natech risk management, particular attention is given to oil spills in water, as they introduce additional complexity due to the unique behaviour of this substance in water and their potential toxicity, as well as the risk of cascading events (i.e. environmental contamination, fires, explosions). The need to develop specific models for simulating oil spills in floodwaters is particularly important, as the existing literature provides numerous models for offshore spills, but knowledge regarding fluvial systems is still limited.This study presents the initial results from the implementation of oil spill routing within the CAESAR-LISFLOOD flood inundation model, which addresses the challenge of solving a simplified shallow water equations using a straightforward numerical approach. This results in a model that is computationally efficient while still being grounded in a solid physical framework. The model is enhanced with a module that simulates the dispersion of oil in floodwaters, accounting for the key processes that influence oil movement in a river system. This implementation allows the model to track the behaviour of an oil slick after a spill in areas with complex topography. It provides valuable insights into the dynamics of the spill, the changes in the slick’s thickness over time, and the extent of the affected area. The model was tested on a case study in Italy, where several simulations were performed for multiple spill scenarios, demonstrating the model’s effectiveness, its ability to accurately simulate the oil spill propagation, as well as its computational efficiency.

Accurate flood damage data are essential for developing reliable flood risk assessments and designing effective risk management strategies. However, empirical flood damage data remain limited, particularly at the object level, hindering the calibration and validation of predictive models. Existing datasets are often highly aggregated and lack the granularity required for detailed analysis. This paper presents two comprehensive, micro-scale datasets documenting flood damage to 256 buildings, comprising both residential buildings and business premises, surveyed in the aftermath of the 2022 flood event in the Marche region of Italy. The georeferenced datasets include information on hazard characteristics, buildings' vulnerability features, physical damage description across structural and non-structural components, indirect damage, and implemented mitigation measures. In addition, original survey forms are provided to support future data collections in different contexts. Datasets and survey forms are available at the link: https://doi.org/10.5281/zenodo.15591850 (Rrokaj et al., 2025). The quality and richness of these datasets make them a valuable resource for improving flood risk modelling and supporting local stakeholders in identifying intervention priorities.

In a climate-changing world, flood events represent one of the most impactful natural hazards, causing severe damage to people and infrastructures. Railway systems are critical infrastructures, susceptible to both structural damage and service disruptions. This study leverages a methodology capable of identifying and classifying paths along the railway system that are vulnerable to fluvial flood hazard and debris-flows. The methodology adopted is DEM-based and suitable for large-scale applications. We hereby focus on Italian Railway Network (IRN) and we consider three flood hazard scenarios, H1 (return period, Tr, up to 500 years), H2 (Tr = 100-200 years) and H3 (Tr = 20-50 years), as defined by the EU Flood Directive and the National Flood Risk Management Plans (FRMP). More specifically, the official FRMP data with national coverage and updated to 2020 are here employed. Across Italy, 26%, 19% and 10% of the IRN is exposed to low, medium and high hazard scenarios (H1, H2 and H3, respectively). To analyze this exposure, we discretize the railway system into sections (average length of 2.53 km) and assess their interaction with flood hazard maps. For each flooded stretch, we characterize the upstream basin using key hydrological parameters, including time of concentration, sub-basin area, river slope, and the presence of debris-flows, as influenced by topography-related triggering thresholds. Based on these parameters, we identified three distinct flood types affecting railroad segments: Very Steep River (VSR) portions characterized by steep slopes and fast hydrological response, Rapid River (RR) stretches with fast-responding watercourses, and Slow River (SR) sections. Each type includes a debris-flow classification determined by the contributing basin's morphological characteristics. The analysis of these flood types reveals that RR floods are predominant, representing 67% of the analyzed flood-prone sections, while SR and VSR floods account for 20% and 13%, respectively. A nationwide dataset is compiled, processed and analyzed in order to provide a comprehensive overview of the IRN affected by floods. This analysis represents a significant step forward in enhancing our understanding of flood dynamics and exposure analysis of railway infrastructure, thereby contributing to more informed decision-making processes in flood risk management and disaster mitigation efforts.

The Po River District Authority promoted the MOVIDA project with the aim to define appropriate methodologies for flood risk assessment and being compliant with the European Floods Directive (Directive 2007/60/EC). A dedicated Open Source Geographic Information System (i.e. QGIS geoprocessing modules) has been developed for mapping the expected damages in all areas at significant risk in the Po District (Northern Italy), considering five categories of exposed elements (population, infrastructures, economic activities, environmental and cultural heritage, and na-tech sites). Focusing on road and railway infrastructures, the methodology proposed within the project adopts information coming from different data sources (Regional Geoportals, Open Street Map, etc.) and allows to qualitatively estimate the potential risk associated with a flood event. Different risk classes (High, Medium, Low and Null) are assigned in relation to roads category (i.e., Highways, Main, Secondary, Service, Other) or railways type (High-Speed train or not), thus considering both the relevance of the infrastructure itself (as well as its topographical characteristics: e.g. tunnel, bridge, etc.) and the magnitude of the expected event (i.e., hazard). The definition of the risk matrix led to the estimation of the lengths of the sections exposed to different risk levels, which is useful to support the definition of potential mitigation measures and support the competent bodies in the organization of the rescue.

Knowledge of river bathymetry is crucial for accurately simulating river flows and floodplain inundation. However, field data are scarce, and the depth and shape of the river channels cannot be systematically observed via remote sensing. Therefore, an efficient methodology is necessary to define effective river bathymetry. This research reconstructs the bathymetry from existing global digital elevation models (DEMs) and water surface elevation observations with minimum human intervention. The methodology can be considered a 1D geometric inverse problem, and it can potentially be used in gauged or ungauged basins worldwide. Nine global DEMs and two sources of water surface elevation (in situ and remotely sensed) were analyzed across two study areas. Results highlighted the importance of preprocessing cross-sections to align with water surface elevations, significantly improving discharge estimates. Among the techniques tested, one that combines the slope-break concept with the principles of mass conservation consistently provided robust discharge estimates for the different DEMs, achieving good performance in both study areas. Copernicus and FABDEM emerged as the most reliable DEMs for accurately representing river geometry. Overall, the proposed methodology offers a scalable and efficient solution for cross-section reconstruction, supporting global hydraulic modeling in data-scarce regions.

In the context of the European Floods Directive, flood risk assessment is a critical component for the definition of an integrated management plan that operates within a multidimensional landscape shaped by intricate interactions. This study explores this complex interplay using a comprehensive framework, aimed at enlightening the non-linear pathways that flood risk assessments can traverse. It adopts the Gioia Methodology within the Grounded Theory approaches, enabling a nuanced exploration of flood risk assessment dynamics. Utilizing data from an Italian case study in the Po River District, this study unveils the flood risk assessment process framework by identifying 13 first-order codes, 6 s-order themes and 3 aggregate dimensions. It introduces a qualitative self-assessment tool to facilitate integration across dimensions and enhance Directive alignment, offering valuable insights for future flood risk assessment implementations.

The impacts of floods on environmental assets are often not assessed. In this communication, we reflect on this issue by analysing the reported environmental consequences of the 2023 Emilia-Romagna floods. The information on the environmental impacts is constructed by collecting data from reports, press releases, and interviews in the aftermath of the events. The most frequently reported damage involves water resources and water-related ecosystems, with cultural and supporting ecosystem services particularly affected. Indirect effects in time and space, intrinsic recovery capacity, cascade impacts on socio-economic systems, and the lack of established monitoring activities appear to be the most challenging aspects for future research.

Flow–duration curves (FDCs) provide a compact view of the historical variability of river flows, reflecting climate conditions and the main hydrologic features of river basins. The Surface Water and Ocean Topography (SWOT) satellite mission will enable the estimation of river flows globally, by sensing rivers wider than 100 m with a sampling recurrence from 3 to 21 days. This study investigated the lifetime mission potential for FDC estimation through the comparison between remotely-sensed and empirical FDCs. We employed the Global Runoff Data Center dataset and derived SWOT-like river flows by selecting gauging stations of rivers wider than 100 m with more than 10-year long daily river flow time series. Overall, 1200 gauged river cross-sections were examined. For each site, we created a set of 24 SWOT-simulated FDCs (i.e., based on different sampling recurrences, mean biases, and random errors) to be compared against their empirical counterparts through the Nash–Sutcliffe efficiency and the mean relative error. Our results show that climate and the sampling recurrence play a key role on the performance of SWOT-based FDCs. Tropical and temperate climates performed the best, whereas arid climates mostly revealed higher uncertainties, especially for high- and low-flows.

The deterioration of superficial water quality is a significant concern in water management. Currently, most European rivers do not achieve qualitative standards defined by Directive 2000/60/EC (Water Framework Directive, WFD), while the health status of many surface water bodies remains unknown. Within this context, we propose a new methodology to perform a semi-quantitative analysis of the pressure state of a river, starting from easily accessible data related to anthropic activities. The proposed approach aims to address the endemic scarcity of monitoring records. This study proposes a procedure to (i) evaluate the relative pressure of different human activities, (ii) identify allocation points of different pollutant sources along the river using a raster-based approach, and (iii) determine a spatial biochemical water quality index. The developed index expresses the overall biochemical state of surface water induced by pollutant sources that may simultaneously impact a single river segment. This includes establishments under the so-called Seveso Directive, activities subjected to the IPPC-IED discipline, wastewater treatment plants, and contaminated sites. The methodology has been tested over three rivers in Northern Italy, each exposed to different industrial and anthropogenic pressures: Reno, Enza, and Parma. A comparison with monitored data yielded convincing results, proving the consistency of the proposed index in reproducing the spatial variability of the river water quality. While additional investigations are necessary, the developed methodology can serve as a valuable tool to support decision-making processes and predictive studies in areas lacking or having limited water quality monitoring data.