The global active satellite population has increased from fewer than 3000 objects in 2020 to more than 14,000 by the end of 2025, while filed megaconstellation plans suggest that tens of thousands of additional satellites may be deployed over coming decades. This rapid expansion of low Earth orbit (LEO) infrastructure has raised concerns regarding novel anthropogenic inputs to the upper atmosphere, particularly black carbon from rocket launches and aluminium oxide nanoparticles generated during satellite re-entry. This review synthesises literature published between 2000 and 2026 across atmospheric chemistry, aerosol science, climate dynamics, and hydrology to evaluate the potential pathways through which these emissions may influence precipitation processes and flood risk. The evidence indicates strong support for several upstream mechanisms, including alumina-mediated ozone chemistry, anthropogenic metal accumulation in stratospheric aerosols, and the disproportionately high radiative forcing efficiency of rocket-derived black carbon. However, substantial uncertainties remain regarding the extent to which these atmospheric perturbations propagate through climate and hydrological systems. This review identifies the current state of knowledge, highlights areas of agreement, uncertainty, and contradiction within the literature, and outlines priority directions for future research, monitoring, modelling, and governance. The findings suggest that while satellite-driven changes to precipitation and flood risk remain unconfirmed, the rapid expansion of megaconstellation activity warrants further investigation within integrated Earth-system frameworks.
Rainfall-induced landslides pose a significant threat to communities and infrastructure in the eThekwini Metropolitan Region, South Africa. This paper presents a geotechnical–hydrogeological property zonation and parameterisation framework developed to support future physically based slope stability modelling. Using a weighted sum analysis in a GIS environment, the landscape was subdivided into distinct property zones by integrating lithology, slope gradient, and landform, with weights derived from a fully reproducible renormalisation of a previously published regional frequency ratio (FR) susceptibility model. This procedure provided the foundation for assigning zone-specific parameters, including effective shear strength parameters (c′ and ϕ′) and saturated hydraulic conductivity (Ksat), derived from laboratory testing, borehole pump testing analysis, and empirical relationships. The approach delineated four geotechnical–hydrogeological zones. A correlation of these zones against an inventory of 819 landslides revealed that over 82% of failures have occurred within Zones 2 and 3. While the hydrogeological conditions of these two susceptible zones range from intermediate to low permeability (Ksat = 10−5 to 10−8 m/s), which promotes transient pore pressure build-up, their high failure frequency corresponds closely with shared low shear strength (c′ = 5 kPa) and comparatively low effective friction angle (ϕ′ = 27.5–30°). This identifies shear strength as an important predisposing control on instability, relative to the inherently more stable Zones 1 and 4 (c′ = 10–15 kPa, ϕ′ = 30–35°). Consequently, this zonation and parameterisation approach advances landslide hazard assessment by providing a reproducible, model-ready dataset intended for future transient rainfall-infiltration simulations (e.g., TRIGRS), laying the groundwork for physically based early-warning systems, and supporting risk-informed urban development.
Education on natural hazards is a non-structural component of Disaster Risk Reduction (DRR), but the evidence base spans curriculum studies, risk-perception research, educational interventions, geospatial approaches and analyses of education-system continuity. This systematic narrative review synthesizes 27 outcome-bearing studies published between 2013 and the partial year 2026. PRISMA 2020 was used as a reporting framework, while PRISMA-S informed a retrospective audit of the search documentation. A structured design-sensitive appraisal recorded evidence family, comparison or temporal structure, outcome directness, permitted inference and principal limitation. The studies were coded into six mutually exclusive primary axes: reviews and frameworks; curriculum and policy; knowledge and risk perception; educational interventions and active methodologies; GIS and geospatial technologies; and educational continuity and system resilience. The included literature suggests that locally situated problems, maps, simulations and inquiry can support knowledge, risk appraisal and preparedness intentions, although demonstrated effects on sustained performance or actual preparedness behavior remain limited. Cross-cutting gaps include weak longitudinal assessment, sparse attention to teacher professional development, limited treatment of indigenous or local knowledge, and no core study centered on learners with disabilities or special educational needs. The review defines critical territorial risk literacy as the capacity to interpret hazard, exposure, vulnerability, capacity and uncertainty through spatial evidence; evaluate their unequal territorial distribution; and translate that understanding into inclusive, proportionate preparedness and collective action.
Landslide susceptibility assessment in data-scarce environments remains challenging. In the northern Andes of Ecuador, the interaction of hypothesized triggers with confounding predisposing factors of landslide occurrences is limited. We investigate the relationship between landslide records and geological, topographic, land use, vegetation and climatic factors in the province of Imbabura using generalized linear models (GLM) and generalized additive models (GAM). Both models are instrumental in identifying that a rainfall increase of one standard deviation in monthly precipitation raises the odds of landslide occurrence, with estimates of 8.84 and 15.84, respectively. The GAM slightly outperformed the GLM by capturing modest non-linear effects, particularly for elevation and profile curvature. Elevation and slope aspect show non-linear and linear tendencies, respectively, although their effects were marginal rather than significant at the 5% level in the selected GAM. Landslides were more likely under wetter conditions at intermediate elevations and on northwestern-facing slopes where ground moisture gradients condition slope stability. Furthermore, the combined effect of agricultural and livestock land uses did not show influence, calling for attention on effects that the scale of analysis did not address. The results provide a baseline framework for rainfall-related landslide occurrence assessment in developing regions with limited data availability.
Unstable rock blocks and cliffs pose a widespread geohazard, and their mechanical state can be tracked through their ambient-vibration resonance frequencies, whose decrease anticipates progressive failure. Such monitoring is usually performed with expensive broadband instrumentation, limiting spatial and temporal coverage. Here we assess whether a low-cost, IoT-enabled node—built around a Raspberry Pi single-board computer, a 24-bit sigma-delta digitiser and a force- balance accelerometer (Geobit FBA-200)—can identify the resonance of an unstable coastal rock block at the celebrated “moving rock” of Kounopetra (Paliki peninsula, Kefalonia, Greece), a site historically renowned for visually perceptible rocking boulders. We stress that the low-amplitude 7.7 Hz structural eigenvibration characterised here is a distinct phenomenon from the historically documented ∼0.3 Hz macroscopic, quasi-rigid rocking of the boulder: the former is the ambient–vibration resonance of the fractured rock mass, the latter a large-amplitude rigid-body oscillation. Two identical nodes recorded ground acceleration simultaneously for nine hours: one on the fractured Kounopetra rock mass and one on stable ground 25 m away, used as a reference. The rock station exhibits a clear, temporally stable fundamental resonance at f0 = 7.7 Hz (Q ≈ 50, damping ζ ≈ 1%), amplified by up to an order of magnitude relative to the reference and entirely absent from it, whereas a narrow 20.5 Hz line present on both nodes is identified as instrument-related and discarded. A simultaneous two-station analysis further shows that the ambient sources are extremely local (only 0.4% of transient activity is common to the two nodes 25 m apart), quantifying a design constraint for differential schemes. The results demonstrate that a low-cost force-balance node is sufficient to establish a resonance baseline for an unstable rock block, opening the way to dense, affordable early-warning networks; the main limitations are the single vertical component and the short record, which preclude polarisation analysis and long-term tracking of f0.
On 18 May 2026, a M5.2 double earthquake struck the Taiyangzhen area of Liunan District, Liuzhou City, Guangxi, China, triggering shallow surface collapses in this karst terrain. We conducted three UAV orthophoto surveys of the meizoseismal area on 20 May, 23 May, and 24 May, and interpreted 17 collapse monitoring units from the imagery. The total collapse area increased from 75.3 m2 on 20 May to 499.1 m2 on 23 May, and further to 553.9 m2 on 24 May. On 20 May, only 7 of 17 units exhibited collapses; by 23 May, all 17 units were affected. Among them, 7 pre-existing collapse patches expanded, and 10 new collapses emerged between 20 and 23 May. Depth measurements revealed measurable depths of 0.18–7.60 m for 7 collapses, and all 6 units with bi-temporal depth data showed continued deepening from 23 to 24 May. Ponding water was observed in up to 10 units, consistent with the 98.7 mm of rainfall recorded during 18–24 May. Multi-temporal UAV surveys reveal that post-seismic surface collapse development in karst terrain extends well beyond the mainshock, with both rapid expansion of pre-existing failures and delayed emergence of new collapses driven by the coupled effects of seismic weakening and hydrologic forcing.
Weathered marl hillslopes fail repeatedly across peri-urban Morocco, yet engineers investigating them rarely know which soil or geometric property most deserves their limited testing budget. This study answers that question for the landslide-prone slopes of Moulay Yacoub, in the northern pre-Rifian domain, by ranking the sensitivity of the factor of safety (FoS) to cohesion (c), friction angle (φ), unit weight (γ), slope height (H) and slope angle (β) under dry, deep-water-table conditions. Latin hypercube sampling generated 400 configurations over ranges drawn from site data; the FoS of each was computed by Bishop’s simplified method in Talren, and regional sensitivity analysis—the two-sample Kolmogorov–Smirnov statistic with a relative sensitivity index—ranked the five inputs. Cohesion governs the response by a wide margin (D = 0.470; 38.2% of total sensitivity at FoS = 1.2), ahead of slope height and friction angle (D ≈ 0.26); slope angle is marginal, and unit weight is not discriminating. Repeating the analysis at four thresholds (1.1–1.4) shows that the primacy of cohesion is threshold-independent, and that the apparent significance of slope angle at stricter thresholds is a statistical power effect, not a mechanical one. Site investigation in comparable marl settings should prioritise cohesion characterisation.
Armenia is located within the Arabia–Eurasia collision zone and is exposed to a significant seismic hazard associated with active fault systems capable of generating destructive earthquakes. The 1988 Spitak earthquake highlighted the vulnerability of Armenian urban areas and the need for reliable seismic risk assessment methods. This study presents the first harmonized scenario-based seismic risk assessment framework for six major Armenian cities by integrating seismotectonic source characterization, deterministic ground-motion modeling, locally derived Vs30-based site characterization, GIS-based exposure modeling, and vulnerability assessment within the ELER (Earthquake Loss Estimation Routine) platform. Vulnerability functions were adapted to Armenian building typologies and calibrated using observed damage from the 1988 Spitak earthquake. Deterministic earthquake scenarios (Mw 6.5–7.3) were developed based on the seismic potential of the country’s principal active fault systems. The results reveal substantial spatial variability in seismic risk controlled by differences in ground-motion intensity, local site conditions, building vulnerability, and population exposure. Masonry-dominated urban areas exhibit the highest relative structural losses, whereas Yerevan experiences the greatest absolute losses because of its large population and concentrated building stock. Severe damage and collapse (D4–D5) may affect more than 20–25% of buildings in the most vulnerable cities. Validation against observed 1988 earthquake damage demonstrates the applicability of the proposed framework for seismic risk reduction, emergency preparedness, and long-term urban resilience planning in Armenia.
The southeastern Tibetan engineering corridor hosts the densest transportation network in Tibet, China, and is traversed by large-scale railway and power corridor projects under construction. This region is home to numerous glacial lakes, some of which are prone to glacial lake outburst floods (GLOFs), posing potential threats to the infrastructure. However, the spatiotemporal evolution and GLOF susceptibility of these lakes remain unclear. Using Landsat 5–9 and Sentinel-2 satellite imagery, we analyzed the spatiotemporal characteristics of glacial lakes from 1990 to 2020. Based on historical GLOF events, we established a susceptibility assessment criterion and determined the susceptibility levels of all glacial lakes in the study area. Results show that the number and area of glacial lakes increased by 40.4% and 26.2%, respectively, from 1990 to 2020, with expansion rates of 2.47 lakes/year and 0.26 km2/year. We identified 31 very highly and 48 highly susceptible lakes, mainly distributed along the Gongrigabu River and the Parlung Tsangpo River. Among them, 35 lakes are most likely to impact National Highways G219 and G318 within the study area. Additionally, three channels with glacial lake clustering amplification effects were found, which may lead to the superposition and amplification of flood impacts, significantly increasing GLOF risks and hazards. Our findings provide crucial references for ensuring the safe operation of local transportation networks and reducing GLOF risks in ongoing large-scale construction projects.
The slopes of the Champagne vineyards are regularly affected by landslides. Given the high societal and economic stakes, these processes cause significant damage and pose a major challenge for the wine industry, forestry and heritage preservation. Numerous studies have already been conducted to understand their behavior and hydrodynamic functioning. They show varied morphologies and a dominant influence of water resources. However, this forcing does not explain the spatial distribution of current landslides, which occur in upper-slope positions, on the steepest terrain recently planted with vines. Using the two shallow landslides at Reuil, located in the heart of the Marne Valley in the Champagne vineyards, as a study site, recent landslide activity is analyzed through a comparative analysis of three DTMs derived from LiDAR HD data and two UAV photogrammetric surveys. This study reveals the affected areas and displaced volumes, which can reach up to 900 m3. Landslide activity was then correlated with regional climatic data. This research shows that landslides are more likely to occur during a period of excessive rainfall following a drier period. Diachronic analysis of aerial images also demonstrates the influence of land use on landslide activity. In particular, land clearing and vineyard operations on the steepest plots constitute a significant anthropogenic forcing affecting slope stability. Taken together, these results provide greater insight into the changing geomorphological dynamics of the Champagne vineyards. On the one hand, they clarify how landslides occur at vineyard plot scale. On the other hand, they provide initial insights into the resilience of stakeholders (winegrowers, etc.) affected by these instabilities.
Accurate characterization of subsurface stratigraphy is essential for reliable landslide stability assessment. However, stratigraphic models constructed solely from sparse borehole data are often constrained by incomplete spatial coverage and substantial interpretive uncertainty. To address this issue, this study developed an integrated probabilistic stratigraphic modeling framework that combines borehole data with electrical resistivity tomography (ERT) data. In the proposed framework, borehole logs provide direct lithological labels and spatial prior information, while the inverted ERT resistivity profile is introduced as a continuous geophysical constraint. Specifically, logarithmic resistivity and the borehole-derived expected stratigraphic configuration were combined into a support vector machine classifier to establish a nonlinear mapping between geophysical responses and stratigraphic categories. A bootstrapping strategy was also used to quantify the stratigraphic uncertainty. The proposed method was then applied to the Panzhuangzu Landslide in Henan Province, China. Based on the probabilistic stratigraphic models, multiple plausible stratigraphic realizations were generated, and their corresponding stability responses are evaluated through numerical analysis. Monte Carlo simulations were further performed to examine how stratigraphic uncertainty propagates into landslide stability predictions. The results show that incorporating ERT data improves the geological plausibility of the inferred stratigraphy. Compared with the borehole-only case, the results obtained from the integrated framework exhibited reduced uncertainty in both the inferred stratigraphic model and the corresponding landslide stability assessment. These findings indicate that the proposed borehole–geophysical data fusion method can provide a more reliable geological basis for landslide stability analysis.
Lake Sarez in Tajikistan, formed by a major earthquake-induced landslide in 1911, is located in the highly seismically active Pamir–Hindu Kush region. The lake is impounded by the Usoi Dam, one of the largest natural landslide dams in the world, which has raised concerns regarding its long-term stability and associated downstream flood hazards. Due to its geomorphological setting and potential exposure to multiple triggering mechanisms, including seismic activity and landslides, Lake Sarez is widely considered a high-consequence hazard system. Although the dam has remained stable for over a century and is currently monitored using modern geodetic and satellite-based technologies, uncertainties remain regarding its internal structure and response to extreme external forcing. While existing early warning systems enhance preparedness in downstream communities, effective long-term risk reduction requires continued monitoring, improved hazard modeling, and strengthened regional cooperation. This review synthesizes existing studies on the geological setting, hazard potential, stability assessments, and disaster risk management strategies related to Lake Sarez. It highlights the importance of integrated multi-hazard analysis and precautionary risk governance in managing low-probability but high-impact natural dam failure scenarios.
NASA’s Fire Information for Resource Management System (FIRMS) provides near-real-time thermal anomaly detections from VIIRS, but not all detections correspond to wildfire incidents: industrial heat, agricultural burning, and sensor artifacts produce false alarms that contribute to alert fatigue for emergency-management analysts. We study whether contextual machine learning (ML) features improve wildfire-incident classification from FIRMS detections, and—more importantly—whether reported gains survive leakage-controlled evaluation. We construct a labeled dataset by matching 521,395 VIIRS SNPP detections across CONUS in 2024 to 3766 NIFC 2024 wildfire perimeters, yielding 131,771 (25.3%) wildfire-matched and 389,624 candidate non-wildfire detections spanning 1067 distinct wildfire incidents. We benchmark five operational baselines and six classifiers under four validation regimes (random, event-aware, 5° spatial-block, and temporal holdout) with and without raw geographic coordinates. A naive random split inflates LightGBM to F1 = 0.985, but a leakage-controlled event-aware split reduces it to F1 = 0.767, and a spatial-block holdout to F1 = 0.627. Feature attribution shows geographic coordinates account for 88.9% of model gain—the summed share of LightGBM’s total split-gain attributed to the three coordinate features within the full-feature model; removing coordinates improves spatial-block generalization from F1 = 0.627 to 0.818, demonstrating that raw coordinates drive memorization of where 2024 fires occurred rather than transferable discrimination. We further show that spatiotemporal clustering must be causal: a model using full-partition clustering appears strong (F1 = 0.908) but leaks future detections, whereas a properly causal trailing-window version ties plain LightGBM in-distribution (F1 = 0.762). Combining causal clustering with no raw coordinates is the most robust configuration under spatial transfer (spatial-block F1 = 0.868 vs. 0.627 for the coordinate model). Bootstrap 95% confidence intervals show these gaps far exceed statistical uncertainty, and sensitivity analyses show the conclusions are robust to the spatial-block size and to the clustering-window choice. Under natural class prevalence (14%), precision falls to 0.69, and results are sensitive to the labeling buffer. All ML models nonetheless far exceed FIRMS high-confidence thresholding (F1 = 0.128). We argue that spatial leakage—not raw accuracy—is the central methodological issue for FIRMS wildfire-incident classification, and recommend coordinate-free, causal spatiotemporal-clustering features evaluated under spatial holdout. The system is intended as an analyst-prioritization decision-support layer, not autonomous incident confirmation.
Integrating geophysical techniques at two contrasting locations—fractured young lavas in southwestern Iceland and older layered basalts in eastern Iceland—constrains the structure and shear-wave velocity of the volcanic subsurface. The results show that relying on a single geophysical method often yields non-unique solutions that can obscure velocity profiles and overlook sharp structural contrasts. Findings from southwestern Iceland reveal that young, faulted ‘a‘ā flows have a complex architecture with shallow, eroded layers, resulting in a reduced Vs30. Conversely, older Miocene bedrock in eastern Iceland shows a well-layered, consolidated structure with a higher Vs30. We demonstrate that modelling Scholte waves from legacy marine seismic data can generate regional velocity models consistent with onshore measurements.
Population growth and unplanned land use significantly contribute to transforming natural hazards into disasters. Earthquake-induced losses of life and property are often linked to inadequate planning decisions. The city center of Şanlıurfa provides a recent example, where the 6 February 2023 earthquake resulted in 340 fatalities and substantial material damage. Variations in urban planning over different periods have caused disaster risk to fluctuate even across short distances. This study examines Şanlıurfa’s urban development in terms of earthquake vulnerability. Using Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP), the earthquake risk map reveals elevated risk in areas near fault lines and regions with high groundwater levels. Approximately 7% of the area is classified as very low risk, 54% as low risk, 37% as moderate risk, and 2% as high risk. Limited consideration of disaster-focused planning has led to both planned and unplanned developments in hazardous zones. Consequently, construction should prioritize low-risk areas, with necessary precautions applied in high-risk zones when unavoidable.
Dynamically grasping the scope of the caving zone and fractured zone in overlying strata is crucial for ground pressure control in sublevel caving mining. Taking Dahongshan Iron Mine as the research object, this study systematically analyzed the evolutionary characteristics of overlying strata caving during sublevel caving mining from 2009 to 2013. Microseismic monitoring was employed as the main method to monitor and locate rock mass fracturing, while roadway monitoring and borehole monitoring were used as auxiliary means to determine the caving boundary and fractured zone scope of overlying strata. Comprehensive analysis of the monitoring data showed that the elevation of the overlying strata caving zone expanded from 930 m to 1215 m, and the width of the fractured zone varied from 50 m to 75 m in different periods. To clarify the rock mass fracture mechanism, P-wave first-motion moment tensor inversion and the Ohtsu moment tensor decomposition method were adopted to classify fracture types. The results indicated that tensile fracturing-related microseismic events accounted for 76.2–80.2% of all events in different periods, demonstrating that tensile failure dominated the fracturing of overlying strata. After December 2012, the caving scope extended to the surface, and a surface collapse area of 290,000 m2 was formed by December 2013, which effectively eliminated the threat of sudden overlying strata caving disasters to the mine. The research results provide reliable technical support for ensuring mine safety production and can serve as a reference for similar sublevel caving mining projects.
This study investigates the comparative effectiveness of Lead Rubber Bearing (LRB) and Friction Pendulum System (FPS) isolation units under varying seismic hazard levels and soil classes, within the framework of the Turkish Building Earthquake Code (TBEC 2018). The assessment was conducted in two stages. First, keeping the site class constant, multiple locations characterized by different seismic hazard levels are examined. Second, a fixed geographical location is considered to evaluate the influence of different site classes on isolator response. The performance of the isolation systems is evaluated in terms of displacement demand, base shear ratio, and code-based verification criteria. Additional sensitivity checks were performed using selected limit values to better understand the response trends under changing hazard and soil parameters. The findings highlight how soil amplification effects and seismic intensity levels influence the relative advantages of LRB and FPSs. The results provide practical insight for the selection of seismic isolation systems in hazard-prone regions, contributing to improved performance-based decision-making in earthquake-resistant design. The isolator parameter choices were set based on average catalogue values provided by manufacturers to make this research an example. As a result of the analysis of the isolators’ performance, it was concluded that the FPS-type isolator performed better as acceleration values increased.
Landslides are a critical environmental hazard in mountainous regions like eastern Uganda, posing serious threats to lives, infrastructure, and ecosystems. While recent advances in geospatial technology have improved hazard assessment, existing research often lacks high-resolution, cloud-based analysis for dynamic landscapes such as the Mount Elgon region. This study addresses that gap by developing a landslide susceptibility map (LSM) using Google Earth Engine (GEE), which integrates remote sensing and geospatial data for scalable analysis. The main objective is to identify landslide-prone zones by analyzing eight conditioning factors, namely slope, elevation, vegetation cover, rainfall, land use land cover, soil type, soil moisture, and groundwater levels using the weighted overlay method (WOM). The methodology produced a classified LSM with zones of high (37.7%), moderate (58%), low (2%), and very low (2.3%) susceptibility, with validation via historical landslide data and ROC analysis yielding an AUC of 0.76, confirming strong predictive performance. The study underscores the value of GEE in hazard modeling and provides actionable insights for targeted risk mitigation, sustainable land use planning, and early warning system development in landslide-prone areas.
Floods increasingly threaten communities and infrastructure in Uganda due to climate variability and land use changes. This study assessed flood hazard, vulnerability, and risk in the Mpanga River Catchment using the Rainfall–Runoff–Inundation (RRI) model integrated with the Analytical Hierarchy Process (AHP). The RRI model showed good performance during calibration (NSE = 0.83) and validation (NSE = 0.71), enabling the generation of hazard maps for different return periods. Results revealed a clear escalation in flood extent with increasing return period, where inundation expanded from about 120.5 km2 in the 5-year event to nearly 348.4 km2 under the 100-year flood scenario. Vulnerability was evaluated through AHP using nine indicators (Land use, population density, distance to river, elevation, rainfall, slope, drainage density, Total Wetness Index, and soil type); however, only Land Use and population density were retained in the final mapping due to data relevance and weight dominance. Combining hazard and vulnerability produced risk maps that revealed most of the catchment falls under low to moderate risk, with high-risk areas concentrated in upstream urbanized zones. Validation with satellite-derived flood maps confirmed model reliability. Evaluation of mitigation strategies showed dams and channel improvements to be the most effective in reducing flood extent. The study provides a practical framework for flood risk management in data-scarce environments, supporting evidence-based planning and interventions.
The aim of this study is to analyze the perceptions of students in Primary Education (5th and 6th grades; n = 260; Valencian Community, Spain) regarding natural hazards, based on their personal experiences, memories, school-based instruction and preventive measures. Methodologically, the study is based on the administration of a mixed-type questionnaire. The high proportion of students reporting strong awareness (68.5%) suggests that school-based instruction is already contributing to a foundational level of risk perception. Floods associated with torrential rainfall are the most frequently recalled hazard, influenced by the cut-off low event of 29 October 2024 (Valencia) (72.3%). Furthermore, no significant differences were found between students who experienced or remembered a flood and those who did not, in terms of preventive measures received, indicating a consistent and homogeneous instruction. In conclusion, this study highlights that students hold a strong awareness of natural hazards, particularly floods, with uniform school-based training. The cut-off low 2024 event in Valencia stood out among other disasters, reinforcing the educational importance of water-related hazards in high-risk regions and underscoring the need to strengthen hydrological awareness as a key component for enhancing socio-territorial risk perception.
