Search Results (14,110)

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

Semi-arid territories in Latin America face chronic water stress; limited observability and fragmented institutions constrain effective water resources management (WRM). This narrative review synthesizes peer-reviewed evidence (2020–2026) on smart technologies that strengthen basin- and utility-level WRM, using Peru (Piura-like coastal semi-arid contexts) as an anchor and Latin America as a comparative lens. We used a structured, traceable database-based workflow and synthesized studies reporting measurable outcomes across five application categories: drought/flood early warning, hydrometeorological forecasting, water quality surveillance, non-revenue water (NRW)/leakage, and allocation and compliance. Findings were organized into an application-oriented taxonomy spanning remote sensing (RS) and GIS, Internet of Things (IoT)/telemetry, analytics/AI-enabled decision support, and hybrid approaches. Evidence most consistently reports operational gains (coverage, timeliness, predictive performance), while governance outcomes are less frequently measured and appear contingent on interoperability, digital capacity, and sustainable operations and maintenance (O&M) conditions. We conclude with a territorial adoption agenda specifying minimum enabling conditions and a phased pathway from pilots to scalable, eco-efficient smart WRM in Peru and comparable semi-arid settings across Latin America. Full article

Monitoring of stable isotopes in throughfall (δ¹⁸O, δ²H) and meteorological parameters is a valuable tool for researching forest hydrology, particularly during extreme events like droughts and floods. This study presents the first systematic analysis of air temperature and precipitation changes over the past 65 years in two Slovenian lowland forests: Murska šuma and Krakovski gozd, in combination with isotopic composition research of throughfall. The observed rising air temperatures and altered precipitation patterns are reflected in the isotopic composition of throughfall. Over the last 65 years, air temperature has increased by approximately 2.5 °C. Although total annual precipitation amounts have remained relatively stable, in the last 35 years there is a notable decrease in precipitation in growing season and an increase during the dormant season, influenced by air masses of Mediterranean origin. Extreme drought in 2022 and flood in 2023 are confirmed by the Standardized Precipitation Index and isotopic variations in throughfall due to fractionation processes. Annual variability appears as seasonal changes, with sine-curve amplitudes of 3.71‰ in Krakovski gozd and 3.61‰ in Murska šuma. Together with the Local Meteoric Water Lines, these patterns support estimates of groundwater mean residence time and the origin of water used by trees. Full article

Multimodal fusion of synthetic aperture radar (SAR) and optical imagery is widely used in Earth observation for applications such as land-cover mapping and surface-water mapping (including post-event flood mapping under near-synchronous acquisitions) and land-use inventory. Optical images provide rich spectral and texture cues, whereas SAR offers all-weather structural information that is complementary but heterogeneous. In practice, this heterogeneity often introduces fusion conflicts in multi-class segmentation, causing critical categories such as water bodies to be under-optimized. To address this issue, this paper presents a SAR-guided class-aware knowledge distillation (SGCAD) method for multimodal semantic segmentation. First, a SAR-only HRNet is trained as a water-expert teacher to learn discriminative backscattering and boundary priors for water extraction. Second, a lightweight multimodal student model (LightMCANet) is optimized using a class-aware distillation strategy that transfers teacher knowledge only within high-confidence water regions, thereby suppressing noisy supervision and reducing interference to other classes. Third, a SAR edge guidance module (SEGM) is introduced in the decoder to enhance boundary continuity for slender structures such as water bodies and roads. Overall, SGCAD improves targeted category learning while maintaining stable performance across the remaining classes. Experiments on a self-built dataset from GF-1 optical and LuTan-1 SAR imagery demonstrate higher overall accuracy and more coherent water/road predictions than representative baselines. Future work will extend the proposed distillation scheme to additional categories and broader geographic scenes. Full article

Urban underground infrastructures are highly vulnerable to intense rainfall events, particularly access stairs, where preferential runoff paths and the most probable evacuation routes can conflict. This study presents a pluvial flood hazard assessment for underground access stairs in the Barcelona Metropolitan Area Metro network. It integrates the EU ICARIA project modeling framework and the hazard assessment criteria based on hydraulic parameters identified by the Spanish national research project FAVOUR. Both current and future climate change rainfall scenarios are considered. The results showed that out of 415 underground access points, 27 face a high risk of floods, while 35 more have potentially high-risk conditions. These figures could rise to 38 (40% increase) and 47 (74% increase) respectively by the end of the century since climate change is projected to increase rainfall intensity and frequency. By quantifying hazard levels across the network, this study allows the identification of points of the infrastructure where hazard conditions can be more critical. Furthermore, the results presented could potentially support targeted adaptation strategies such as entrance retrofitting, improved drainage design, and emergency planning to develop resilient and sustainable cities. The proposed methodology demonstrates how ICARIA’s modeling framework can effectively evaluate and anticipate flood hazards in complex urban environments at the asset level. Full article

This study evaluated the performance of a smart farming technology (SFT) and a climate-smart agriculture (CSA) approach for improving irrigation management in pecan (Carya illinoinensis) orchards in México through soil moisture monitoring, evapotranspiration estimation, and real-time data integration. Continuous monitoring allowed irrigation to be maintained at field capacity, preventing plant stress while avoiding total soil saturation or permanent wilting point. Calibration of soil moisture sensors showed a very strong correlation (R² = 0.99) between sensor reverse voltage and volumetric soil water content in predominant sandy loam soils, confirming the reliability of the monitoring system for irrigation scheduling. Seasonal analysis of reference evapotranspiration (ETo) and crop evapotranspiration (ETc) revealed increasing atmospheric water demand during summer months, with crop coefficient (Kc) values ranging from approximately 0.3 during dormancy to 1.0–1.3 during peak vegetative growth. After five years of field implementation of the technology, results showed water savings exceeding 50% compared with traditional flood irrigation practices. The optimized irrigation schedule reduced total seasonal irrigation depth from 216 cm to 128 cm, representing a 59% reduction in applied water while maintaining adequate soil moisture conditions for crop development at field capacity (FC). These results highlight the potential of integrating sensor-based monitoring, evapotranspiration modeling, and IoT platforms to enhance water-use efficiency and support sustainable pecan production under increasing climate variability. Full article

Urbanization fundamentally fractures the natural water cycle, leading to a cascade of interconnected problems including increased flood risk, degraded water quality, stressed groundwater resources, and inefficient distribution networks. Traditional, fragmented management approaches that address these issues in isolation have proven inadequate. This research argues for a paradigm shift towards an Integrated Urban Water Management (IUWM) framework anchored in the concept of the “river-aquifer-pipe network continuum”, treating these components as a single, dynamic hydrological and infrastructural entity. Drawing upon a series of detailed case studies from Eastern Romania, this paper synthesizes the systemic impacts of development across the entire urban water system. Evidence from the Prut, Olt, and Bahlui river basins demonstrate how channelization exacerbates flood peaks and leads to severe biochemical degradation. Hydrogeological modeling of the Gherăești-Bacău wellfield reveals the vulnerabilities of over-extraction, while analysis of the Iași water network highlights the challenge of water losses in the aging infrastructure. In response, a modern, multi-tool approach is consolidated into a practical, three-stage framework for action: Diagnose, Prescribe, and Optimize. This framework advocates for (1) a comprehensive diagnosis using a suite of predictive numerical models (a “digital twin”); (2) the prescription of foundational, nature-based solutions, such as floodplain restoration, to heal core ecological functions; and (3) the continuous optimization of engineered infrastructure using smart, real-time control technologies. The synthesis concludes that an integrated, data-driven, and collaborative approach is the only sustainable path forward. Future research should focus on formally coupling these diagnostic models to create true Digital Twins of urban water systems—an essential step towards building resilient, water-secure cities for the 21st century. Full article

This study presents a GIS-driven multi-criteria decision analysis (MCDA) framework for regional suitability screening of data center (DC) development in the United Kingdom. The methodology integrates spatial exclusion of constrained zones, raster standardization of climate and infrastructure indicators, Analytic Hierarchy Process (AHP) weighting, and Weighted Linear Combination (WLC) to generate a national suitability surface at 1 km resolution. Climate indicators (temperature, air frost days, humidity, and solar radiation) and infrastructure and environmental constraint indicators (grid access, transport proximity, environmental protections, and population distribution) were standardized and combined within a GIS-based decision framework. Hard constraints such as protected areas and flood zones were applied through binary exclusion, while climatic and infrastructure factors were evaluated using weighted suitability scoring. Five candidate regions were identified from the suitability analysis: the Scottish Highlands, Northeast England, Southwest England (Cornwall), Northwest England, and Eastern England. These regions were further evaluated against key requirements including power infrastructure accessibility, workforce and connectivity availability, and exposure to environmental and hydro-climate constraints. The final comparison identified Lincolnshire as the most suitable region due to strong grid accessibility, favorable composite climate suitability, adequate population proximity, and limited overlap with protected areas. The proposed framework demonstrates how climate-driven cooling suitability can be integrated with infrastructure accessibility and environmental constraints within a unified spatial decision model for national-scale digital infrastructure planning. Full article

Riverine bridges are critical infrastructure that are increasingly exposed to severe hydrological hazards. This study proposes and validates a synergistic methodology for the assessment of riverine bridge resilience, integrating the conceptual 4R framework (robustness, rapidity, resourcefulness, and redundancy) with field inspections, hydrological and hydraulic modeling, including scour evaluation, within a multicriteria analysis scheme. The methodology comprises: (i) a systematic review of literature and regulations to construct a 30-parameter matrix across five dimensions (technical, economic, social, organizational, and environmental); (ii) data acquisition through field inspections, detailed topography, and technical studies; and (iii) one-dimensional hydraulic modeling in HEC-RAS under extreme scenarios (return periods of 100 to 750 years and a critical 500 m³/s scenario representing a potential overflow of the Aguada Blanca reservoir). The Bridge Resilience Index (BRI) is computed through a weighted additive model and a sensitivity analysis. Application to the San Martín Bridge (Arequipa, Peru), a structure with more than 60 years of service and recurrent preventive closures during flood events, revealed critical conditions: minimum freeboard of 0.26 m, absence of hydraulic protections, and limited institutional capacity. The resulting BRI value (1.898) indicates a low resilience level. The proposed framework provides a useful tool for risk-informed decision-making, the prioritization of interventions, and the strengthening of resilience in critical infrastructure. Full article

Flood hazard mapping remains challenging in regions with limited hydrological and topographic data, despite increasing flood risk driven by climate change and land-use dynamics. This study aims to demonstrate that preliminary flood inundation maps can be developed under data-scarce conditions by integrating limited field observations with publicly available datasets and simplified hydrodynamic modeling. The Khlong Wat watershed in southern Thailand, where flood hazard maps had not previously existed despite recurrent flood events, was used as a case study. Flood simulations were conducted using the HEC-RAS model with a simplified terrain representation to approximate river bathymetry, acknowledging uncertainties in channel geometry. Hydrodynamic results show a systematic increase in flood extent and depth with increasing flood recurrence intervals, with inundated areas expanding from 1.43 km² for a 10-year flood to 4.02 km² and 5.97 km² for 100- and 500-year events, respectively. Agricultural land is consistently the most affected category, accounting for more than two-thirds of the flooded area across all scenarios, with rubber plantations being the dominant land use. Urban exposure increases with flood magnitude, although most buildings remain affected by shallow inundation below 0.5 m. The results confirm that meaningful flood hazard assessments can be achieved in data-limited regions and provide a transferable framework to support flood risk management and spatial planning in similar environments. Full article

This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda. Full article

Jasmonic acid (JA) and its derivative, methyl jasmonate (MeJa), are naturally occurring plant hormones involved in alleviating abiotic stresses, such as exposure to extreme temperatures (cold or heat), flooding and drought. JA content increased following MeJa applications at pre- or postharvest, regulating several physiological and biochemical processes during fruit growth and ripening. As a preharvest treatment, MeJa increased crop yield and improved the organoleptic quality of the fruit. Regarding postharvest applications, MeJa reduced the chilling injury symptoms in sensitive fruits when they were stored at cold temperatures. In addition, there is some evidence of crosstalk between JA and other plant hormones. In this review, we highlight the mechanisms by which jasmonates contribute to plant stress resistance, regulating the biosynthesis and metabolism of abiotic stress and improving fruit quality. Full article

Quantifying river surface flow velocity is essential for hydrodynamic modelling, flood forecasting, and water resource management. Traditional in situ methods provide accurate point measurements but are costly and limited in spatial coverage. Unmanned aerial vehicles (UAVs) offer a flexible, non-contact alternative for high-resolution monitoring. Optical flow is a tracer-independent technique for deriving velocity fields from RGB video, making it well suited to UAV-based surveys. However, its operational use is hindered by the limited availability of annotated datasets and by instability under low-texture or noisy conditions. This study combines a Finite element method (FEM)-based physical flow model with UAV video to generate reference datasets and introduces a modified Recurrent All-Pairs Field Transforms (RAFT) architecture based on multiframe sequences. A Gated Recurrent Unit fusion module (Fuse-GRU) is incorporated prior to correlation computation, improving robustness to illumination changes and surface homogeneity while maintaining computational efficiency. The proposed model delivers stable, physically consistent velocity estimates across multiple rivers and flow conditions. Accuracy improves with higher spatial resolution and moderate temporal spacing. Compared to field measurements, the average angular difference ranged from 8 to 15°. The high error values were mainly caused by inaccuracies in the physical model and by complex river features. These findings confirm that multiframe optical flow can reproduce realistic river flow patterns with accuracy comparable to physically-based simulations, thereby supporting UAV-based hydrometric monitoring and model validation. Full article

In the field of oil reservoir engineering, the development of large-dip-angle reservoirs poses significant challenges due to their strong heterogeneity, pronounced gravity effects, and inefficient water flooding sweep, all contributing to suboptimal oil recovery rates. This study aims to address these challenges by focusing on the core issue of optimizing water injection development strategies for such reservoirs. A numerical simulation mechanism model is constructed based on actual large-dip-angle reservoir A, and the impact of key parameters—including reservoir dip angle, permeability, injection–production well spacing, water injection intensity, and crude oil viscosity—on oil recovery is systematically analyzed under the “water injection at structural lows and oil production at structural highs” high-pressure water injection development mode. The simulation results reveal that the oil recovery rate increases with higher dip angles, permeability, injection–production well spacing, and water injection intensity; however, excessive water injection intensity or crude oil viscosity can lead to premature water breakthrough, reducing efficiency. Using the analytic hierarchy process, the primary controlling factors are ranked as permeability > crude oil viscosity > reservoir dip angle > water injection intensity > injection–production well spacing. Furthermore, development theory charts are established to guide the selection of appropriate water injection intensities for different injection–production well distances and permeabilities. This study offers valuable theoretical insights for optimizing water injection development in large-dip-angle reservoirs, thereby enhancing oil recovery and economic benefits and laying a foundation for future research and practical applications in similar reservoir settings. Full article

The combined impacts of urban pluvial flooding and traffic congestion can severely delay emergency response. However, existing studies often focus on isolated scenarios, failing to systematically quantify the reduction in overall service capability and specific route disruptions to critical functional nodes under compound hazards. To address this problem, this study proposes a three-tier analytical framework to systematically evaluate the resilience of emergency services under compound hazards. The framework first utilizes spatial network analysis to simulate the overall spatial evolution of service capabilities for Emergency Medical Service (EMS) and Fire and Rescue Service (FRS) across various return periods and traffic conditions. It then delves into the emergency response coverage for vulnerable population places. Finally, the fastest-route analysis is employed to identify variations in rescue routing. The study reveals several critical insights. (1) As rainfall intensity and traffic congestion intensify, the coverage areas of EMS and FRS exhibit significant contraction and boundary erosion. Notably, the service areas of FRS show a distinct fragmentation pattern. (2) The protection levels for vulnerable population places (e.g., kindergartens, primary and secondary schools, and nursing homes) show a pronounced stepwise decline. Under extreme rainfall and the heaviest congestion, the 5 min coverage for these sites drops from 89.9% to 23.6% for EMS, and from 72.4% to only 15.1% for FRS, revealing a severe risk exposure for vulnerable groups. (3) Road inundation leads to a substantial extension of rescue routes and even results in the complete isolation of 141 primary and secondary schools. Overall, the framework provides actionable decision support to enhance urban emergency response under compound hazards. Full article