Search Results (302)

Community disaster resilience is increasingly guiding risk-reduction investments, but in many Southeast European settings, comparable subnational data remain scarce. This study assesses perceived community disaster resilience across Serbia by combining BRIC–DROP dimensions into a single index and analyzing differences across hazard types and sociodemographic factors. A cross-sectional household survey was conducted using multistage random sampling and the “next birthday” method for respondent selection. The final sample included 1200 adults from 22 local government units across four regions: Belgrade, Vojvodina, Šumadija & Western Serbia, and Southern & Eastern Serbia. Participants evaluated preventive measures and societal resilience for ten hazard types and considered five social dimensions: social structure, social capital, social mechanisms, social equity/diversity, and social beliefs. Descriptive statistics, bivariate analyses (including Pearson correlations, t-tests, and ANOVA), and multiple linear regression identified key predictors of preventive behavior and perceived resilience. Composite scores highlighted spatial resilience differences. Overall perceptions were generally low, mostly falling below the midpoint of the scale. Furthermore, the highest ratings for implemented preventive measures were recorded for pandemics/epidemics, storms/hail, and floods, whereas the lowest were observed for environmental pollution and droughts. Perceived resilience was highest for snowstorms, storms/hail, and pandemics/epidemics, and lowest for environmental pollution and droughts. Also, respondents reported relatively strong family ties and favorable perceptions of communication and access to basic supplies, but weak institutional capacity, particularly in budget allocation, early warning and public notification, rapid decision-making, and evacuation and shelter readiness. Regression results were statistically significant but explained only a small portion of the variance. Age and public-sector employment positively predicted perceived resilience; fear, income, and, to a lesser extent, education were negatively associated. These findings highlight the structural and psychosocial factors that shape perceptions of resilience. The BRIC–DROP composite indicates generally low perceived preparedness and resilience, especially in risk communication, evacuation and shelter readiness, and financing—the key bottlenecks in strengthening local resilience. The results recommend combining institutional reform with targeted risk communication to reduce fear and build trust, especially focusing on hazard areas with the lowest confidence, such as environmental pollution and drought. Full article

Cultural heritage sites in arid regions are often underestimated in terms of flood risk; however, the increasing frequency and intensity of extreme precipitation events under climate change have significantly amplified threats to these fragile environments. Taking the Xixia Imperial Tombs in China as a case study, this research investigates strategies for flood hazard prevention and control for cultural heritage in arid areas. By situating the study within the broader context of climate change and global heritage conservation, the paper examines the impacts of flooding on heritage sites and the historical evolution of flood control measures. It further integrates an analysis of the site’s geographical characteristics, traditional flood management structures, and contemporary conservation practices. The study systematically elucidates the compound risks of “drought–desertification–sudden flooding” faced by cultural heritage in arid landscapes. The findings suggest that heritage protection should transition from reactive, post-disaster restoration toward proactive preventive conservation. This shift requires the integration of both engineering and non-engineering measures, supported by technology-based systems such as environmental monitoring and early warning platforms, to establish a comprehensive risk management framework. The research highlights that overcoming the prevailing misconception that “arid regions are free from flood risks,” embedding heritage flood management into regional planning, and ensuring legal, financial, and interdisciplinary cooperation are essential for the long-term safeguarding of cultural heritage in arid environments. This study offers practical insights and a transferable reference for the protection of heritage sites in similar climatic and geographical contexts worldwide. Full article

Due to climate change, the frequency and intensity of extreme rainfall events have increased in South Korea, resulting in recurrent urban flooding that exceeds the design capacity of conventional drainage systems. In the Dangjin Traditional Market area, comparable rainfall conditions in 2024 and 2025 caused repeated flooding, suggesting that structural improvements implemented without quantitative verification do not necessarily guarantee effective flood prevention. This study aims to support sustainable urban flood management by assessing the pre-implementation effectiveness of structural flood mitigation measures using a spatially explicit simulation approach. An ArcGIS-based rainfall–inundation simulation was conducted by integrating a 1 m LiDAR-derived digital elevation model, land cover data classified using a pixel-based Support Vector Machine, detailed building and channel datasets, and observed hourly rainfall from the July 2025 extreme event. Scenarios with and without the application of levee heightening and drainage capacity expansion were compared under identical rainfall conditions. The results indicate that the application of structural measures leads to a clear reduction in inundation extent and water depth. The proposed framework provides a practical simulation-based decision-support tool for verifying flood mitigation measures in advance and for promoting sustainable flood risk management in urban areas prone to recurrent flooding. Full article

Hydropower development in high-altitude regions increasingly confronts a challenging “trilemma”: high hydraulic heads, large unit discharges, and spatially constrained narrow valleys. Under such conditions, conventional energy dissipation measures frequently fail to prevent downstream riverbed scour, thereby threatening both ecological integrity and infrastructure safety. This study aims to propose, parametrically optimize, and physically validate a novel composite energy dissipation structure designed to resolve this specific trilemma based on a pressure-dividing transition mechanism. Using the Louli Hydropower Project as a case study (Q_max = 6944 m³/s, unit discharge q = 119 m³/(s·m), available basin length L = 78 m), we conducted systematic 1:100 scale physical model tests. The results demonstrate that conventional optimizations, such as secondary stilling basins and dentated sills, are ineffective under these boundary conditions, leading to incomplete hydraulic jumps and extended high-velocity zones. In contrast, the proposed composite structure, which integrates a deepened stilling basin (depth = 9 m), asymmetric sidewall widening (20 m offset), and a gentle slope transition (1:20 gradient), achieved superior performance. Under the 50-year design flood with controlled discharge operation, the energy dissipation rate increased significantly from 32.11% (baseline) to 63.49% (composite) at the end sill. Furthermore, the structure reduced comprehensive turbulence intensity by 17.8% and floor slab impact stress by 23.4%. These findings validate the composite system as a sustainable solution for high-head dams in constrained settings, offering benefits for riverbed protection and structural durability. Full article

Ice-jam formation during winter low-flow conditions represents a persistent hydrotechnical hazard in small and medium-sized rivers of Central Europe. Despite extensive monitoring efforts, preventive structural measures remain insufficiently developed and rarely evaluated under real geomorphological constraints. This study proposes and hydraulically verifies a low-profile riverbed sill designed to suppress the initiation and stabilization of frazil and anchor ice during critical winter discharges. The analysis integrates 20 years of hydrological and water-temperature data (2004–2024), 26 detailed cross-sectional surveys, a high-resolution longitudinal profile derived from DMR 3.0, and a newly formulated Ice-Jam Risk Index (Iice) combining flow velocity, depth-to-width ratio and thermal deficit. Application to the Torysa River (rkm 42.8–43.6) revealed a clearly defined high-risk zone (rkm 43.20–43.38), where hydraulic conditions frequently fall below the critical thresholds for ice accumulation (U < 0.35 m·s⁻¹; h/B < (h/B)_crit; ΔT > 0.5 °C), indicating shallow and laterally widened channel sections prone to anchor-ice stabilization. Model simulations demonstrated that the proposed sill increases mean velocity by 22–35% during Q₆₅–Q₈₅ conditions, reducing the local I(ice) by 61%, while preserving the conveyance capacity for discharges above Q₅₀ and avoiding measurable backwater impacts upstream. Field-based morphology, risk index interpolation and hydraulic modeling all confirm that the structure effectively disrupts the formation of stable anchor-ice nuclei, which have historically triggered severe ice-jam floods in this reach (2011/12, 2016/17, 2021/22). The results show that a properly dimensioned low-profile sill provides a passive, low-cost, and transferable engineering solution for winter flood risk mitigation, outperforming reactive ice-management techniques while maintaining ecological and hydraulic compatibility with small natural rivers. The methodology is replicable for other rivers where supercooling, low-flow hydraulics and channel morphology jointly control ice-jam initiation. Full article

African grasses deliberately introduced for cattle forage have become among the most destructive invaders of tropical wetlands globally, yet invasion mechanisms and management strategies remain poorly understood. We conducted field experiments examining competition dynamics between the invasive African grass Echinochloa pyramidalis and native wetland species in La Mancha, Mexico—a Ramsar site of international importance. Experiment 1 tested invasion potential within native Sagittaria lancifolia zones using four treatments: control, herbicide removal, E. pyramidalis transplant, and combined removal + transplant. Repeated-measures ANOVA showed significant treatment and time effects on invasion success, with vegetation removal facilitating invasion (relative importance value increasing from 0 to 149.4 ± 26.6 after 18 months) while transplants alone failed to establish (RIV < 7.0). Sagittaria maintained 35–48% biomass across treatments, demonstrating coexistence capacity. Experiment 2 examined natural invasion of the vegetation ecotone over 49 months. Mixed-effects models revealed that E. pyramidalis increased dominance in its zone (β = 9.98, z = 4.77, p < 0.001) but showed minimal expansion into the adjacent Sagittaria habitat, indicating propagule limitation rather than competitive exclusion as the invasion constraint. Sagittaria removal within E. pyramidalis zones significantly reduced invasion temporal increase (β = −6.44, z = −2.18, p = 0.030), suggesting biotic resistance. Results demonstrate that E. pyramidalis possesses invasion potential but requires disturbance to overcome establishment barriers. These findings support prevention-based management prioritizing disturbance limitation in intact wetlands and demonstrate that hydrological management maintaining permanent flooding (>30 cm depth) can effectively control established invasions by exploiting C4 photosynthetic limitations. Conservation implications for Mexican coastal wetlands—which lack legal protection equivalent to mangroves despite comparable ecosystem services—are discussed. These findings inform evidence-based management of African grass invasions in tropical wetlands worldwide. Full article

Flooding is a recurrent problem in many Brazilian cities, resulting in significant losses that affect health, assets, finance, and the environment. The uncertainty regarding extreme rainfall events due to climate change makes this challenge even more severe, compounded by inadequate urban planning and the occupation of risk areas, particularly for the municipality of Catu, in the state of Bahia, which also suffers from recurrent floods. Critical hotspots include the Santa Rita neighborhood and its surroundings, the main supply center, and the city center—the municipality’s commercial hub. The focus of this research is the unprecedented quantification of the socioeconomic impact of these floods on the low-income population and the region’s informal sector (street vendors). This research focused on analyzing and modeling the destructive potential of intense rainfall in the Santa Rita region (Supply Center) of Catu, Bahia, and its effects on the local economy across different recurrence intervals. A hydrological simulation software suite based on computational and geoprocessing technologies—specifically HEC-RAS 6.4, HEC-HMS 4.11, and QGIS— 3.16 was utilized. Two-dimensional (2D) modeling was applied to assess the flood-prone areas. For the socioeconomic impact assessment, a loss procedure based on linear regression was developed, which correlated the different return periods of extreme events with the potential losses. This methodology, which utilizes validated, indirect data, establishes a replicable framework adaptable to other regions facing similar socioeconomic and drainage challenges. The results revealed that the area becomes impassable during flood events, preventing commercial activities and causing significant economic losses, particularly for local market vendors. The total financial damage for the 100-year extreme event is approximately US $30,000, with the loss model achieving an R² of 0.98. The research concludes that urgent measures are necessary to mitigate flood impacts, particularly as climate change reduces the return period of extreme events. The implementation of adequate infrastructure, informed by the presented risk modeling, and public awareness are essential for reducing vulnerability. Full article

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R² = 0.87) and artificial neural networks (ANNs) performed best for EFR (R² = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R² ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change. Full article

Debris flows represent a significant geohydrological hazard, impacting the surrounding environment and threatening human settlements by altering ecological equilibria. The formation of temporary, often unstable, natural dams that obstruct normal river flow and create secondary flood risks poses a complex and prolonged threat to the sustainable management of water resources. Non-invasive risk assessment and analysis tools are therefore essential for addressing this challenge effectively. In this context, this study uses an end-to-end numerical modelling approach validated on an actual river obstructed in past by a debris flow. The simulation focused on sustainable risk management after the landslide dam rupture. This computational methodology is a non-invasive technology that provides a fundamental alternative to costly and environmentally invasive field techniques for assessing the risk of complex river systems. Two separate numerical simulations were carried out using the HEC-RAS code. The first simulation used the integrated sediment transport module to quantify the dynamics of solid material deposition and dilution. The second simulation modelled secondary flooding scenarios using the dam break simulation module. The aim of integrating these non-invasive simulations is to analyse the interaction between the river and debris accumulation, understand the river’s natural regeneration capacity and determine the hydraulic response to sudden dam failure. These results are essential for geohydrological risk assessment and mitigation, thereby improving the effectiveness of prevention measures and systemic resilience against landslides. Full article

Against the backdrop of insufficient accuracy and adaptability of satellite precipitation products in complex terrain areas, this study focused on the Min River Basin (MRB) on the eastern edge of the Qinghai–Tibet Plateau. A two-step machine learning fusion framework was established, which integrates precipitation event identification and quantitative intensity estimation in a systematic manner. This framework incorporated 5 precipitation products (PERSIANN-CDR, CMORPH, GSMaP, IMERG, MSWEP), measured data, and environmental variables. The study compared the precipitation estimation performance of Random Forest (RF), Extreme Learning Machine (ELM), eXtreme Gradient Boosting (XGBoost), Bagging, and Double Machine Learning (DML) models, and analyzed the models’ performance under different precipitation intensities and altitudes, as well as their variable sensitivity. The results showed that: (1) DML models outperformed Single Machine Learning (SML) models and original precipitation products, with RF-Bagging being the optimal model. The daily-scale Correlation Coefficient (CC) of RF-Bagging was over 50% higher than that of original products, while the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were reduced by more than 40% and 35%, respectively. (2) For moderate-to-heavy precipitation, the RF-Bagging and RF-RF models maintain a stable Critical Success Index (CSI) of 0.7. In high-altitude regions, their Probability of Detection (POD) approaches 1, and the Heidke Skill Score (HSS) is 30–40% higher than that in mid-altitude areas, significantly outperforming other models and demonstrating strong adaptability to complex terrain. For light precipitation, while the POD values of these two models are comparable to those of other models, their False Alarm Rate (FAR) is reduced by 15–20%, effectively mitigating precipitation false alarms. (3) GSMaP, IMERG, and MSWEP were the core input variables for all models. RF and ELM models were more dependent on environmental variables, while XGBoost and Bagging models relied more on satellite data. This framework can provide technical references for precipitation estimation in complex terrain areas and contribute to watershed water resource management as well as flood prevention and mitigation. Full article

Climate change is advancing, sea levels are rising, and peak river discharges are increasing. Accelerated sea level rise (SLR) may pose a significant threat to the long-term habitability of the Netherlands. In the short term, further reinforcement of flood defenses is required. However, the key long-term question is which adaptation strategy will most effectively manage flood risk in the Netherlands. As part of the SLR Knowledge Programme, research was conducted on various long-term strategies, focusing on the feasibility of three approaches: Protect, Advance, and Accommodate. The Protect and Advance strategies aim to reduce flood risk primarily through the prevention of flooding. The Accommodate strategy, particularly in its more extreme form, emphasizes Managed Retreat, following the precautionary principle, or seeks to mitigate flood consequences rather than invest in Prevention. This study examined the economic implications of two opposing cornerstone strategies, Protect and Managed Retreat, as well as hybrid strategies that integrate elements of both, across different sea level rise scenarios. Additionally, the study includes a forward-looking assessment of the potential impacts on the financial sector, with particular attention to catastrophe insurance and capital requirements aimed at mitigating default risk. The findings indicate that a Managed Retreat strategy represents a last-resort option and cannot be implemented effectively without concurrent protective measures. Furthermore, the annual flood risk is only marginally reduced under the Accommodate strategy, even when combined with protective interventions, while its associated costs significantly exceed those of the Protect strategy. A combined approach integrating protection with localized Accommodate measures that support multi-functional land use, such as nature-based solutions and water storage, appears to offer a more promising strategy, if these values cover the costs. The results can be used to evaluate the effectiveness of possible adaptation strategies to sea level rise. Full article

Background. Climate change is increasingly recognized as a threat to mental health, giving rise to constructs such as eco-anxiety and solastalgia. Although these phenomena have gained attention, quantitative data from European populations remain scarce. Objectives. This study investigated the prevalence and correlates of eco-anxiety in an Italian sample, focusing on associations with demographics, environmental disaster exposure, psychological distress, psychosis-risk indicators, and quality of life. Methods. A cross-sectional online survey was conducted with 1051 participants. Measures included the Hogg Eco-Anxiety Scale (HEAS-13), Brief Symptom Inventory (BSI), Prodromal Questionnaire (PQ-16), SF-36 Health Survey, and a socio-demographic/environmental exposure questionnaire. Data were analyzed using correlations, t-tests, and regression analyses. Results. Eco-anxiety was higher among younger participants, women, and individuals without children, while education showed a weak negative association. No differences emerged by rural vs. urban residence. Participants exposed to floods, droughts, wildfires, and landslides reported elevated eco-anxiety. Scores correlated strongly with general distress (r = 0.57), positively with psychosis-risk indicators (PQ-16 distress, r = 0.42), and negatively with quality of life (r = −0.25). Conclusions. Eco-anxiety in Italy is linked to socio-demographic vulnerabilities, disaster exposure, and impaired mental health. These findings highlight eco-anxiety as a pressing public health concern and stress the need for prevention and interventions that promote adaptive coping and engagement with climate change. Full article

A utility tunnel is a complex underground facility that serves as a critical infrastructure integrating and operating systems such as electricity, telecommunications, and drainage within a city. However, various hazard events such as fire, flooding, condensation, damage, and intrusion can occur within utility tunnels, posing risks not only to the degradation of facility functions but also to potential human casualties and economic losses. Therefore, it is crucial to establish prompt and effective response measures for these hazard events. Unlike previous studies that focused on individual hazard types, this study proposes an integrated and standardized multihazard response framework for utility tunnels. Through case analysis, fire, flooding, condensation, damage, and intrusion were defined as representative hazard events, and the response stages for each were classified into attention, caution, warning, and critical levels. Appropriate response scenarios were developed for each stage, providing prompt and efficient response measures tailored to different situations. The proposed framework offers a unique contribution by presenting a unified structure that supports stage-based management and can be directly applied to smart monitoring and control systems in underground infrastructure. This study is expected to contribute to improving the disaster prevention and response systems of utility tunnels and enhancing overall facility safety. Full article

In this research, an innovative, integrative method is applied, which not only links media discourse and statutory planning documents but also involves both quantitative and qualitative analysis. By going beyond the traditional extreme of either policy review or text-based SETS frameworks, this study becomes the pioneer of a dual-coded, matrix-driven approach, which is capable of measuring policy–implementation gaps and empirically revealing the impact of media framing on disaster management outcomes. The 29 October 2024 Valencia flood, which claimed over 229 lives, highlights critical shortcomings in the region’s flood management policies. This study evaluates media and institutional sources to examine how public discourse aligns with post-flood management strategies. It focuses on Valencia’s statutory flood management plan, the “Pla d’acció territorial de caràcter sectorial sobre prevenció del risc d’inundació a la Comunitat Valenciana” (“Regional Action Plan for Flood Risk Prevention,” PATRICOVA) and its limited integration with the Socio–Ecological–Technological Systems (SETS) framework, which we identify as a central weakness. By analyzing Spanish media coverage, particularly from sources such as El País, ABC, and La Vanguardia, alongside government policy documents, the study reveals a gap between theoretical flood risk planning and practical disaster response. Our keyword-based text mining of leading newspapers highlights the neglect of social, ecological, and technological interactions. While PATRICOVA emphasizes nature protection and technological infrastructure, it overlooks critical societal dimensions and climate adaptation scenarios. Media analysis reveals significant failures at the SETS interfaces, especially in early warning systems, intergovernmental coordination, and community preparedness. Full article

Mountain regions are highly susceptible to severe hydro-meteorological events. These events induce substantial morphological changes that are preserved in the environment and cause significant economic losses, representing a major challenge for water resource management. Due to their abrupt nature, mitigating the impacts of such events requires preventive measures. The goal of the study was to comprehensively evaluate the impact of severe hydro-meteorological events on the mountain environment of the Ochotnica catchment, considering both environmental and economic aspects, over several years. This multi-year perspective also provided the opportunity to formulate some recommendations for the development of adaptation strategies for extreme hydro-meteorological events in mountain areas. The study demonstrates that delineation of the Maximum Probable Flood (MPF) hazard zone is a key element in building resilience to such events in mountain areas. Information related to the extent and depth of this zone, together with flow velocity, are critical components which may support actions aimed at reducing flood exposure and vulnerability, limiting the negative consequences of extreme hydro-meteorological events in mountain catchments prone to flash floods. Full article