Search Results (742)

Floods are considered one of the most devastating natural hazards, frequently resulting in substantial loss of lives and widespread damage to infrastructure. In the period of 4–7 September 2023, the region of Thessaly experienced unprecedented rainfall rates due to Storm Daniel, which caused significant flooding and many damages and fatalities. The southeastern areas of Trikala were among the many areas of Thessaly that suffered the effects of these rainfalls. In this research, a flood susceptibility assessment (FSA) of the broader area surrounding the settlement of Megala Kalyvia is carried out through the analytical hierarchy process (AHP) as a multicriteria analysis method, using Geographic Information Systems (GIS). The purpose of this study is to evaluate the prolonged flood susceptibility indicated within the area due to the past floods of 2018, 2020, and 2023. To determine the flood-prone areas, seven factors were used to determine the influence of flood susceptibility, namely distance from rivers and channels, drainage density, distance from confluences of rivers or channels, distance from intersections between channels and roads, land use–land cover, slope, and elevation. The flood susceptibility was classified as very high and high across most parts of the study area. Finally, a comparison was made between the modeled flood susceptibility and the maximum extent of past flood events, focusing on that of 2023. The results confirmed the effectiveness of the flood susceptibility assessment map and highlighted the need to adapt to the changing climate patterns observed in September 2023. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

The urban drainage system is a significant lifeline for ensuring the safe operation of a city. In recent years, defects and diseases in drainage pipes and their ancillary facilities have occurred frequently. Aiming to provide decision-makers with comprehensive benefit evaluation support, we chose to evaluate the security, environmental, social, and economic benefits of urban drainage culverts and pipes (UDCPs). An index system of 14 first-level indicators in four dimensions was established, and the indicators contain 28 influencing factors. The index weight was obtained by combining the analytical hierarchy process and entropy weight method, and the weights assigned to the security, environmental, social, and economic benefits were 0.448, 0.222, 0.202, and 0.128, respectively. The evaluation system was developed on the basis of a geographic information system (GIS), and the topological analysis of the GIS was applied in the calculation. To process the questionnaire results, this study adopted the automatic questionnaire analysis and scoring method combining natural language processing and optical character recognition technology. The method was applied in the study area in southern China, which contains 9 catchment areas and 1356 pipes. The results show that about 5% of the pipelines need to be included in the renewal plan. For UDCP renewal, the findings provide a decision-making tool of the comprehensive analysis for the selection of engineering technologies and the evaluation of the implementation effects. Full article

The main purpose of this study is to understand the potential determinants of sustainable field crop farm productivity. This paper considers a multi-input, multi-output production technology to estimate the effects of aridity on farm-level productivity using a stochastic input distance function. By isolating the respective weather components of agricultural total factor productivity (TFP), we can better assess the impact on productivity of adopting various technologies and farm practices that might otherwise be masked by changing climate conditions or weather shocks. We make use of data from Phase 3 of the United States Department of Agriculture (USDA) Agricultural Resource Management Survey (ARMS) between 2006 and 2020. We supplement this estimation using field crop farm productivity determinants, including technology adoption and farm practice variables derived from the ARMS Phase 2 data. We identify several factors that affect farm productivity, including many practices that help farmers make more sustainable use of natural resources. The results show that adopting yield monitoring technology, fallowing in previous years, adding or improving tile drainage, and contour farming each improved farm productivity. In particular, during our study period, conservation tillage increased by over 300% across states on average. It is estimated to increase productivity level by approximately 3% for those adopting this practice. Critically, accounting for local weather effects increased the estimated productivity of nearly all farm practices and increased the statistical significance of several variables, indicating that other TFP studies that did not account for climate or weather effects may have underestimated the technical efficiency of farms that adopted these conservation practices. However, the results also show the impacts can be heterogeneous, with effects varying between farms located in the U.S. northern or southern regions. Full article

Various nature-based solutions (NbS)—such as constructed wetlands, drainage ditches, and vegetated buffer strips—have recently demonstrated strong potential for mitigating pollutant transport in open channels and river systems. Numerical modeling is a widely adopted and effective approach for assessing the performance of these interventions. This study presents the first development of a two-dimensional (2D) meshless advection–diffusion model based on an Eulerian smoothed particle hydrodynamics (SPH) framework, specifically designed to simulate passive pollutant transport in open channel flows. The proposed model marks a pioneering application of the ESPH technique to environmental pollutant transport problems. It couples the 2D depth-averaged shallow water equations with an advection–diffusion equation to represent both fluid motion and pollutant concentration dynamics. A uniform particle arrangement ensures that each fluid particle interacts symmetrically with eight neighboring particles for flux computation. To represent the pollutant transport process, the dispersion coefficient is defined as the sum of molecular and turbulent diffusion components. The turbulent diffusion coefficient is calculated using a prescribed turbulent Schmidt number and the eddy viscosity obtained from a Smagorinsky-type mixing-length turbulence model. Three analytical case studies, including one-dimensional transcritical open channel flow, 2D isotropic and anisotropic diffusion in still water, and advection–diffusion in a 2D uniform flow, are employed to verify the model’s accuracy and convergence. The model demonstrates first-order convergence, with relative root mean square errors (RRMSEs) of approximately 0.2% for water depth and velocity, and 0.1–0.5% for concentration. Additionally, the model is applied to a laboratory experiment involving 2D pollutant dispersion in a 90° junction channel. The simulated results show good agreement with measured velocity and concentration distributions. These findings indicate that the developed model is a reliable and effective tool for evaluating the performance of NbS in mitigating pollutant transport in open channels and river systems. Full article

In recent decades, the Brazilian Cerrado has faced rapid land conversion, resulting in the loss of approximately half of its original vegetation cover. Most existing conservation units within the biome are increasingly threatened by the expansion of land use around their boundaries. The establishment of buffer zones with land use regulations may protect biodiversity within these protected areas. In this study, we evaluated and ranked the 10 km buffer zones of 15 national parks (NPs) located in the Cerrado biome, identifying their priority for biodiversity conservation and sustainable land use interventions. The analysis considered the following data: land use and land cover change from 2012 to 2020, extent of natural vegetation fragments, presence or absence of state and municipal conservation units within the buffer zones, and drainage density. Two multicriteria analysis methods, the analytic hierarchy process and the weighted linear combination, were applied to classify the buffer zones into five levels of threat: very high, high, moderate, low, and very low. Among the 15 buffer zones analyzed, 11 were classified as having high to very high priority for conservation actions. The buffer zones surrounding the Serra da Bodoquena, Emas, Canastra, and Brasília NPs were ranked as having very high priority. Between 2012 and 2020, the most severe reductions in ecological connectivity were observed in the buffer zones of Grande Sertão Veredas (44.5%), Nascentes do Rio Parnaíba (40.4%), and Serra das Confusões (36.7%). Given the relatively high proportion of natural vegetation in the buffer zones located in the northern Cerrado, we recommend prioritizing conservation efforts in this region. In contrast, in the southern portion of the biome, where land occupation is more intense, strategies should focus on promoting environmentally sustainable land use practices. Full article

Objectives: This research aims to examine the impact of intracystic hemorrhage (ICH) on therapeutic outcomes in children with macro or mixed cystic lymphatic malformation (cLM). Methods: This retrospective study included macro/mixed cLM cases with or without ICH who underwent treatment between January 2019 and June 2024. All patients were diagnosed using preoperative imaging findings and intraoperative indocyanine green (ICG) lymphography. The baseline data of enrolled cases were retrospectively collected. The clinical characteristics were documented, including gender, age, histological typing, location, maximum diameter, and intracystic condition. Patients with or without ICH were divided into two groups. The dependent variables for predicting an excellent outcome were analyzed using multivariable logistic regression models after adjusting for potential factors using a univariable regression model. Postoperative variables, including duration of negative drainage, local infection, scar hyperplasia, and follow-up, were compared between the two groups. Results: A total of 83 cLM patients were included (ICH group: n = 36 and without ICH group: n = 47). A complete absence of afferent lymphatic vessels was demonstrated using intraoperative ICG lymphography, suggesting the isolated nature of ICH cases. ICH (p = 031; OR, 2.560; 95% CI, 1.089–6.020) was identified as the main predictor, and younger patients (p = 035; OR, 0.415; 95% CI, 0.183–0.940) had a lower potential for excellent outcomes. For the postoperative variables, the ICH group exhibited a shorter duration of negative drainage than the without ICH group (p < 0.001), while no significant differences were found regarding local infection (p = 0.693) and scar hypertrophy (p = 0.648). Conclusions: Although characterized by aggressive progression and compressive symptoms, ICH emerges as an independent favorable prognostic predictor in macro/mixed cLM management, potentially attributable to its isolated nature. Full article

The integration of traditional geomorphological approaches with advanced artificial intelligence techniques represents a promising frontier in flood risk assessment for arid regions. This study presents a comprehensive analysis of the Wadi Ranuna basin in Medina, Saudi Arabia, combining detailed morphometric parameters with advanced Geospatial Artificial Intelligence (GeoAI) algorithms to enhance flood susceptibility modeling. Using digital elevation models (DEMs) and geographic information systems (GISs), we extracted 23 morphometric parameters across 67 sub-basins and applied XGBoost, Random Forest, and Gradient Boosting (GB) models to predict both continuous flood susceptibility indices and binary flood occurrences. The machine learning models utilize morphometric parameters as input features to capture complex non-linear interactions, including threshold-dependent relationships where the stream frequency impact intensifies above 3.0 streams/km², and the compound effects between the drainage density and relief ratio. The analysis revealed that the basin covers an area of 188.18 km² with a perimeter of 101.71 km and contains 610 streams across six orders. The basin exhibits an elongated shape with a form factor of 0.17 and circularity ratio of 0.23, indicating natural flood-moderating characteristics. GB emerged as the best-performing model, achieving an RMSE of 6.50 and an R² value of 0.9212. Model validation through multi-source approaches, including field verification at 35 locations, achieved 78% spatial correspondence with documented flood events and 94% accuracy for very high susceptibility areas. SHAP analysis identified the stream frequency, overland flow length, and drainage texture as the most influential predictors of flood susceptibility. K-Means clustering uncovered three morphometrically distinct zones, with Cluster 1 exhibiting the highest flood risk potential. Spatial analysis revealed 67% of existing infrastructure was located within high-risk zones, with 23 km of major roads and eight critical facilities positioned in flood-prone areas. The spatial distribution of GBM-predicted flood susceptibility identified high-risk zones predominantly in the central and southern parts of the basin, covering 12.3% (23.1 km²) of the total area. This integrated approach provides quantitative evidence for informed watershed management decisions and demonstrates the effectiveness of combining traditional morphometric analysis with advanced machine learning techniques for enhanced flood risk assessment in arid regions. Full article

Landslides are among the most destructive natural hazards in the Western Ghats region of Kerala, driven by complex interactions between geological, hydrological, and anthropogenic factors. This study aims to generate a high-resolution Landslide Susceptibility Level Map (LSLM) using a machine learning (ML)-based Random Forest (RF) model integrated with Geographic Information Systems (GIS). A total of 231 historical landslide locations obtained from the Bhukosh portal were used as reference data. Eight predictive factors—Stream Order, Drainage Density, Slope, Aspect, Geology, Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Moisture Stress Index (MSI)—were derived from remote sensing and ancillary datasets, preprocessed, and reclassified for model input. The RF model was trained and validated using a 50:50 split of landslide and non-landslide points, with variable importance values derived to weight each predictive factor of the raster layer in ArcGIS. The resulting Landslide Susceptibility Index (LSI) was reclassified into five susceptibility zones: Very Low, Low, Moderate, High, and Very High. Results indicate that approximately 17.82% of the study area falls under high to very high susceptibility, predominantly in the steep, weathered, and high rainfall zones of the Western Ghats. Validation using Area Under the Curve–Receiver Operating Characteristic (AUC-ROC) analysis yielded an accuracy of 0.890, demonstrating excellent model performance. The output LSM provides valuable spatial insights for planners, disaster managers, and policymakers, enabling targeted mitigation strategies and sustainable land-use planning in landslide-prone regions. Full article

As the urban underground natural gas pipeline network expands, the explosion risk arising from methane accumulation in drainage ditches due to pipeline leakage has increased severely. A two-dimensional numerical model—9.7 m in length (including a 1-m obstacle section), 0.1 m in diameter, and with a water volume fraction of 0.2—was developed to address the flexible boundary characteristics of urban underground ditches. The investigation examined the influence of methane concentration on explosion propagation characteristics. Results indicated that, at a methane concentration of 11%, the peak pressure attained 157.9 kPa, and the peak temperature exceeded 3100 K—all of which were significantly higher than the corresponding values at 10%, 13%, and 16% concentrations. Explosion-induced water motion exerted a cooling effect that inhibited heat and pressure transfer, while obstacles imposed partial restrictions on flame propagation. Temporal profiles of temperature and pressure exhibited three distinct stages: “initial stability–rapid rise–attenuation”. Notably, at a methane concentration of 16%, the water column formed by fluid vibration demonstrated a pronounced cooling effect, causing faster decreases in measured temperatures and pressures compared to other concentrations. Full article

This study evaluated the effects of reduced glutathione (GSH) application on the production and quality of sour passion fruit irrigated with brackish water in the semi-arid region of Paraíba, Brazil. The experiment was conducted in drainage lysimeters under greenhouse conditions at the Center of Technology and Natural Resources of the Federal University of Campina Grande (UFCG). Treatments combined five levels of electrical conductivity of brackish irrigation water (Bw: 0.4, 1.2, 2.0, 2.8, and 3.6 dS m⁻¹) and four GSH concentrations (0, 40, 80, and 120 mg L⁻¹), arranged in a randomized block design with three replicates. Salinity levels above 0.4 dS m⁻¹ negatively affected fruit production and post-harvest quality of ‘BRS GA1’ sour passion fruit. Foliar application of 120 mg L⁻¹ GSH increased fruit yield, while 74 mg L⁻¹ GSH mitigated salt stress effects on production and pulp chemical quality. The ‘BRS GA1’ cultivar was highly sensitive to salinity, showing a 26.9% yield reduction per unit increase in Bw electrical conductivity above 0.4 dS m⁻¹. The results suggest that GSH can alleviate salt stress damage, improving crop productivity and fruit quality under semi-arid conditions. Full article

Urban pluvial flooding (UPF) is an increasingly critical issue due to rapid urbanization and intensified precipitation driven by climate change that yet remains understudied in the Caribbean. This study analyzes the effects of UPF resulting from the transformation of a natural karstic landscape into an urbanized area considering a sub-watershed in Chetumal, Southern Mexican Caribbean, as a case study. Hydrographic numerical modeling was conducted using the IBER 2.5.1 software and the SCS-CN method to estimate surface runoff for a critical UPF event across three stages: (i) 1928—natural condition; (ii) 1998—semi-urbanized (78% coverage); and (iii) 2015—urbanized (88% coverage). Urbanization led to the orthogonalization of the drainage network, an increase in the sub-watershed area (20%) and mainstream length (33%), flow velocities rising 10–100 times, a 52% reduction in surface roughness, and a 32% decrease in the potential maximum soil retention before runoff occurs. In urbanized scenarios, 53.5% of flooded areas exceeded 0.5 m in depth, compared to 16.8% in non-urbanized conditions. Community-based knowledge supported flood extent estimates with 44.5% of respondents reporting floodwater levels exceeding 0.50 m, primarily in streets. Only 43.1% recalled past flood levels, indicating a loss of societal memory, although risk perception remained high among directly affected residents. The reported UPF effects perceived in the area mainly related to housing damage (30.2%), mobility disruption (25.5%), or health issues (12.9%). Although UPF events are frequent, insufficient drainage infrastructure, altered runoff patterns, and limited access to public shelters and communication increased vulnerability. Full article

Green roofs are increasingly being adopted as sustainable, nature-based solutions for managing urban stormwater, mitigating the urban heat island effect, and saving energy in buildings. However, the long-term performance of their individual components—particularly filter geotextiles—remains understudied, despite their critical role in maintaining system functionality. The filter layer, responsible for preventing clogging of the drainage layer with fine substrate particles, directly affects the hydrological performance and service life of green roofs. While most existing studies focus on the initial material properties, there is a clear gap in understanding how geotextile filters behave after prolonged exposure to real-world environmental conditions. This study addresses this gap by assessing the mechanical and structural integrity of geotextile filters after five years of use in both extensive and intensive green roof systems. By analyzing changes in surface morphology, microstructure, and porosity through tensile strength tests, digital imaging, and scanning electron microscopy, this research offers new insights into the long-term performance of geotextiles. Results showed significant retention of tensile strength, particularly in the machine direction (MD), and a 56% reduction in porosity, which may affect filtration efficiency. Although material degradation occurs, some geotextiles retain their structural integrity over time, highlighting their potential for long-term use in green infrastructure applications. This research emphasizes the importance of material selection, long-term monitoring, and standardized evaluation techniques to ensure the ecological and functional resilience of green roofs. Furthermore, the findings contribute to advancing knowledge on the durability and life-cycle performance of filter materials, promoting sustainability and longevity in urban green infrastructure. Full article

The rapid expansion of urban areas has increased the prevalence of impermeable surfaces, intensifying flooding risks by disrupting natural water infiltration. Permeable pavements have emerged as a sustainable alternative, capable of reducing stormwater runoff, improving surface friction, and mitigating urban heat island effects. Nevertheless, their broader implementation is often hindered by issues such as clogging and limited mechanical strength resulting from high porosity. This study examines the design of interlocking permeable blocks utilizing ultra-high-performance concrete (UHPC) to strike a balance between enhanced drainage capacity and high structural performance. A topology optimization (TO) strategy was applied to numerically model the ideal block geometry, incorporating 105 drainage channels with a diameter of 6 mm—chosen to ensure manufacturability and structural integrity. The UHPC formulation was developed using particle packing optimization with ordinary Portland cement (OPC), silica fume, and limestone filler to reduce binder content while achieving superior strength and workability, guided by rheological assessments. Experimental tests revealed that the perforated UHPC blocks reached compressive strengths of 87.8 MPa at 7 days and 101.0 MPa at 28 days, whereas the solid UHPC blocks achieved compressive strengths of 125.8 MPa and 146.2 MPa, respectively. In contrast, commercial permeable concrete blocks reached only 28.9 MPa at 28 days. Despite a reduction of approximately 30.9% in strength due to perforations, the UHPC-105holes blocks still far exceed the 41 MPa threshold required for certain structural applications. These results highlight the mechanical superiority of the UHPC blocks and confirm their viability for structural use even with enhanced permeability features. The present research emphasizes mechanical and structural performance, while future work will address hydraulic conductivity and anticlogging behavior. Overall, the findings support the use of topology-optimized UHPC permeable blocks as a resilient solution for sustainable urban drainage systems, combining durability, strength, and environmental performance. Full article

The crystallization of soluble salts is one of the most significant agents of deterioration affecting porous building materials in historical architecture. This process not only compromises the physical integrity of the materials but also results in considerable aesthetic, structural, and economic consequences. Soluble salts involved in these processes may originate from geogenic sources—including soil leachate, marine aerosols, and the natural weathering of parent rocks—or from anthropogenic factors such as air pollution, wastewater infiltration, and the use of incompatible restoration materials. This study examines the role of capillary rise as a primary mechanism responsible for the vertical migration of saline solutions from the soil profile into historic masonry structures, especially those constructed with calcareous stones. It describes how water retained or sustained within the soil matrix ascends via capillarity, carrying dissolved salts that eventually crystallize within the pore network of the stone. This phenomenon leads to a variety of damage types, ranging from superficial staining and efflorescence to more severe forms such as subflorescence, microfracturing, and progressive mass loss. By adopting a multidisciplinary approach that integrates concepts and methods from soil physics, hydrology, petrophysics, and conservation science, this paper examines the mechanisms that govern saline water movement, salt precipitation patterns, and their cumulative effects on stone durability. It highlights the influence of key variables such as soil texture and structure, matric potential, hydraulic conductivity, climatic conditions, and stone porosity on the severity and progression of deterioration. This paper also addresses regional considerations by focusing on the context of Spain, which holds one of the highest concentrations of World Heritage Sites globally and where many monuments are constructed from vulnerable calcareous materials such as fossiliferous calcarenites and marly limestones. Special attention is given to the types of salts most commonly encountered in Spanish soils—particularly chlorides and sulfates—and their thermodynamic behavior under fluctuating environmental conditions. Ultimately, this study underscores the pressing need for integrated, preventive conservation strategies. These include the implementation of drainage systems, capillary barriers, and the use of compatible materials in restoration, as well as the application of non-destructive diagnostic techniques such as electrical resistivity tomography and hyperspectral imaging. Understanding the interplay between soil moisture dynamics, salt crystallization, and material degradation is essential for safeguarding the cultural and structural value of historic buildings in the face of ongoing environmental challenges and climate variability. Full article