Search Results (6,017)

To address the stability issues of bridge piers on high expansive soil slopes in the Yangtze-Huaihe River Water Transfer Project and reveal the slope-bridge structure interaction mechanism, this study performed 100 g geotechnical centrifuge model tests. Slope failure modes under rainfall-bridge load coupling are investigated, with bridge pier deformation, earth pressure, and pile bending moment evolution analyzed. Results show that rainfall-induced failure causes shallow slope sliding with negligible pier displacement, keeping the structure safe. Conversely, under bridge working and ultimate loads, the slope will experience a mid-deep landslide with a sliding depth of 13–20 m, leading to slope instability and bridge overturning. The influence range of shallow landslides is 1–2 m, and the earth pressure at the pile cap is 132 kPa, which is a critical factor affecting bridge stability. In contrast, the bearing performance of pile foundations plays a dominant controlling role in deep-seated landslides. With the increase in landslide depth, the inflection point of the pile gradually moves downward. Numerical simulations further indicate that shallow landslides feature superficial slip–shear failure, and deep-seated landslides follow a progressive slip tensile cracking mechanism. Full article

The assessment of climate variability impacts on crop production and price varies by what factors studies consider, including annuals and perennials. Unlike annual crops, climate impacts on perennial crops like coconuts require a multiple-year assessment. Although previous studies have examined climate effects on coconut production, there is a critical gap in understanding multiple-year impacts of climate variability on coconut production and price. Therefore, this paper aims to fill this gap by assessing the extent to which climate variability affects coconut production and prices in Sri Lanka, the fourth largest coconut producer in the world. For this purpose, we analyzed rainfall, temperature, average drought months, coconut production, coconut cultivation area, and coconut retail price from 2010 to 2022. We then created and administered five regression models to illustrate the impact of climate variables for a single year and multiple years on coconut production, yield, and price. The results indicate that rainfall in the previous year is the most critical determinant for production (p = 0.014) and yield (p = 0.032), while drought intensity and temperature shocks show delayed negative effects on production. Lagged temperature shocks and supply shortages significantly increased nominal coconut retail prices. A temperature increase by 1 °C in the previous year raised prices by approximately LKR 36 per nut. After adjusting for inflation, only temperature (p = 0.002) effects was found significant, indicating that climate-induced supply constraints dominate real price changes. Our three-year analysis showed that drought conditions, together with rainfall and temperature variability, reduced production with a delayed effect (p = 0.026). These findings highlight the importance of incorporating multiple-year climate impacts into adaptation and price stabilization policies for coconut and other perennial crops. Full article

Urban hydrological risks are endangering vulnerable populations, particularly in densely populated metropolitan areas undergoing rapid land use transformation. This study uses geospatial analysis to identify zones in the Athens metropolitan area that are prone to surface water accumulation and stream flow development during extreme rainfall events. Two spatial indices were developed by integrating digital elevation models, flow accumulation, slope, aspect, the topographic wetness index, and classified road network data: a Surface Water Accumulation Index and a Stream flow Pathway Index. Roads were categorized based on their orientation relative to the direction of the slope, which allowed for an assessment of their influence on hydrological flow. Both indices were classified into five risk levels representing gradients of hydrological vulnerability. The spatial patterns revealed by this analysis show strong correlations with flood-prone areas and natural drainage systems. These insights are essential for guiding urban planning efforts aimed at reducing hydrological hazards, particularly for at-risk groups such as the homeless. This approach offers a valuable tool for promoting sustainable, socially inclusive landscape management. Full article

Agricultural rivers are often silent receivers of microplastics (MPs) from diffuse, non-point sources; however, their pollution dynamics during rainfall events remain poorly understood. In this study, MP transport was investigated at three sampling points in an agricultural river catchment, where mulching films are used, and sewage sludge is not applied. Sampling was conducted in the Umeda River and its tributaries during six sampling events. MP flux exhibited a strong positive correlation with river discharge (L–Q relationship; n = 1.49–1.61, R² = 0.67–0.87). The L–Q model indicates that a tenfold increase in discharge results in approximately a 600-fold increase in MP flux and a 1000-fold increase in total suspended solid flux. MP abundance during rainfall was up to four times higher than that during baseflow, ranging from 73 ± 64 to 200 ± 111 particles/m³, while peak flux reached 6736 particles/s, with an MP mass of 811 mg/s. Regarding particle characteristics, rainfall enhanced the heterogeneity of MPs, although fragments and polyethylene/polypropylene polymers remained consistently dominant across all hydrological stages. First-flush behavior was observed at HU, with more than half of the total MP mass exported within the initial 50% of the event flow volume. These findings help to inform mitigation strategies that should prioritize a reduction in upstream plastic inputs in order to effectively manage MP transport in agricultural rivers. Full article

A vulnerability surface modeling method based on dual intensity metrics is proposed to assess the impact of typhoons and heavy rainfall disasters on the distribution pole-tower conductor system. A three-dimensional finite-element model is developed for a typical “three-pole four-conductor” distribution line, considering the uncertainties in both load-side and structural-side parameters. A spatially coherent turbulent wind field is generated using the Davenport spectrum and harmonic superposition method, while an equivalent rain load is derived based on raindrop spectrum integration. Nonlinear dynamic time-history analysis is then conducted under multiple combinations of basic wind speeds and rainfall intensities, extracting engineering demand parameters such as conductor axial tension and pole-base bending moments. Based on probabilistic demand analysis, the relationship between engineering demand parameters and dual intensity measures is regressed in the logarithmic domain to construct bivariate fragility surfaces for both the conductors and the poles. Critical failure curves are obtained by intersecting the fragility surfaces with the 10% exceedance probability level, enabling rapid classification of structural risk under the joint effects of wind and rain. The results show that the regression model provides a high fit, effectively revealing that wind speed is the dominant control factor, while rainfall intensity serves as a secondary amplifying factor. The resulting critical failure curves can be directly used as operation and maintenance warning thresholds and can be coupled with observed and forecast meteorological data for time-varying risk assessment. These findings provide methodological support and engineering guidance for risk assessment, operation and maintenance decision-making, and resilience enhancement of distribution networks under multi-hazard coupling. Full article

Compound geological disaster chains pose major challenges for disaster prevention in mountainous regions due to their complex mechanisms and cascading impacts. This study investigates a landslide–debris flow–flash flood hazard chain that occurred on 21 July 2024 in the Xujia River catchment, Mianning County, Sichuan Province, China. This event is used as a representative case to improve the understanding of the formation and amplification mechanisms of breach-type debris flows through dynamic inversion constrained by sedimentary records. The objective is to reconstruct the evolution of the event and assess its downstream hazard extent. Post-disaster sedimentary and geomorphological records, including deposit distribution, channel aggradation, and flow traces, were systematically analyzed based on remote sensing interpretation, unmanned aerial vehicle surveys, and detailed field investigations. These sedimentary data were used as key constraints to estimate debris flow magnitude and mobility under different rainfall scenarios. A rainfall flood scenario-based estimation method was applied to quantify debris flow magnitude, and numerical simulations were conducted using the Rapid Mass Movement Simulation model to reproduce debris flow propagation and deposition processes. The results indicate that prolonged antecedent rainfall triggered slope failure in a tributary, leading to the accumulation of landslide-derived material and the formation of a temporary channel blockage. The subsequent breach of this blockage significantly amplified debris flow discharge, velocity, and sediment outflow, resulting in downstream hazard expansion. Simulation results constrained by sedimentary evidence show that peak discharge and solid material output under breach conditions were approximately three times higher than those of rainfall-driven scenarios under comparable rainfall frequencies. These findings demonstrate that sedimentary records provide critical constraints for the inversion of landslide debris flow disaster chain dynamics and highlight the effectiveness of post-disaster evidence based numerical assessment for hazard analysis and risk mitigation in debris flow-prone mountainous catchments. Full article

Urban flooding is prevalent in low-lying, coastal regions, where subtle topographic variation, shallow groundwater, and impervious surfaces govern inundation dynamics. This study evaluates urban flood susceptibility across Miami-Dade County by integrating flood-conditioning factors, including elevation, slope, rainfall, land use/land cover, distance to roads and open water, stream power index (SPI), topographic wetness index (TWI), groundwater depth, and flow accumulation within an Analytical Hierarchy Process (AHP)-based weighted overlay framework. The AHP-derived weights demonstrated strong consistency (consistency ratio = 0.022) and were applied to reclassify each conditioning factor into five flood susceptibility classes—very low to very high. The model performance was evaluated using the Federal Emergency Management Agency (FEMA) flood zone, and the findings demonstrated that the AHP-based framework effectively differentiates flood susceptibility at a fine urban scale, achieving strong predictive performance; area under the Curve (AUC) = 0.85. The results also reveal pronounced spatial variability in flood susceptibility, with northeastern urbanized areas, particularly in Hialeah, Miami Gardens, Miami Lakes, and Downtown Miami, exhibiting higher susceptibility compared to the northwestern Everglades region. Overall, this study presents a robust urban flood susceptibility framework that supports improved flood risk assessment and decision-making in complex urban coastal environments. Full article

Rain-on-snow events in cold regions frequently trigger slope failures. This study elucidates the instability mechanism of low-permeability silty clay slopes under combined rainfall and snowmelt conditions. A refined numerical model was established based on the sequential coupling of SEEP/W and SLOPE/W, utilizing the Morgenstern-Price method for stability analysis. A rigorous mesh sensitivity analysis confirmed that a locally refined mesh of 0.2 m with exponential time-stepping is essential to eliminate numerical dispersion at the wetting front. Simulation results indicate a significant time-lag effect in stability response; the critical failure time lags behind rainfall cessation (e.g., ~8 h for moderate rain) due to gravity-driven moisture redistribution. Spatially, the slope toe reaches saturation first, generating excess pore-water pressure and suggesting a tendency toward retrogressive instability. Furthermore, snowmelt superposition functions as a continuous hydraulic load, creating a base flow effect that advances the acceleration phase of failure by 1–2 h and further reduces the minimum safety factor. These findings highlight the critical role of the slope toe saturation and the necessity of considering snowmelt intensity in landslide early warning systems for cold regions. Full article

Floods represent one of the most frequent and damaging natural hazards in Mediterranean mountain regions, where intense rainfall and complex topography amplify runoff and inundation risk. This study aims to delineate flood-susceptible zones in the Monti Lucretili area of central Italy, an environmentally sensitive and culturally significant landscape that hosts archeological remains and UNESCO listed dry-stone heritage using an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) approach. Fifteen (15) conditioning factors, including elevation, slope, rainfall, soil, lithology, land use/land cover, drainage density, and proximity to rivers and roads, were derived from open-access satellite remote sensing and spatial datasets. The AHP model produced a flood susceptibility index ranging from 1.806 to 4.465, reclassified into five categories from very low to very high zones. The resulting map indicates that low- and moderate-susceptibility zones dominate the study area, while high and very high classes are primarily concentrated along valleys and drainage corridors. Model validation indicates strong regional-scale predictive performance, with 85.36% of modeled flood-prone areas located within high- to very-high-susceptibility zones and an AUC value of 0.82. Overall, the study highlights the potential of open-access AHP–GIS modeling as a practical screening tool for flood susceptibility assessment and heritage-aware spatial planning in Mediterranean environments. Full article

Combined sewer overflow (CSO) pollution has consequently become a critical challenge, yet its formation depends on tightly coupled dry- and wet-weather processes. This study aims to integrate high-resolution field monitoring with statistical analysis to characterize the full “accumulation–transport–discharge” cycle of CSO pollution in a representative combined sewer catchment located in the Yangtze River basin, China. A dynamic analytical framework was established, combining multiple pollution media and linking dry-weather accumulation with rainfall-driven transport, enabling quantitative source apportionment of pollutant contributions. Results indicated that during dry periods, domestic sewage exhibited strong enrichment, with concentrations of total inorganic nitrogen (TIN), chemical oxygen demand (COD), and total phosphorus (TP) being 2.1-, 2.3-, and 1.9-fold higher, respectively, than the Chinese secondary discharge standards (GB 18918-2002). Surface sediment showed pronounced spatial heterogeneity, with greater loads in residential than transportation areas and substantial fine-particle accumulation on roofs (particle size < 150 μm, accounting for 73% by mass). Sewer sediments, dominated by coarse inorganic particles (over 77% by mass), represented the main pollutant reservoir. Rainfall produced distinct hydrodynamic and water quality responses. Light rain following long antecedent dry periods generated a high-concentration but low-load regime with a strong first flush, whereas moderate rain yielded lower concentrations but higher loads. Overflow occurred when rainfall exceeded ~14 mm, with pollutant peaks lagging rainfall by 20–45 min in the studied area. TIN and TP peaked sharply at rainfall event onset, and first-flush intensities followed TIN > TP > COD > suspended solids (SS). Source apportionment identified sewer sediments as the dominant CSO source, followed by surface runoff and domestic sewage. These findings clarify the mechanisms linking dry-weather accumulation to wet-weather transport and support targeted CSO pollution control and urban water quality management. Full article

Groundwater modeling in semi-arid regions presents significant challenges due to complex aquifer dynamics, limited data availability, and heterogeneous hydrogeological conditions. This study presents a comprehensive comparative analysis of three fuzzy expert system strategies for monthly groundwater level forecasting in the Al-Hsain Basin, Syria: localized models based on hydrogeographical grouping, a unified basin-wide approach, and an innovative behavioral clustering methodology. Using synchronized rainfall and temperature data from 35 monitoring wells over four years (2020–2024), we developed and evaluated fuzzy inference systems’ directional classification accuracy as the primary performance metric, categorizing groundwater level changes into rise, stable, and decline states rather than predicting continuous values. This choice reflects the qualitative nature of fuzzy expert systems and their suitability for groundwater management under data-limited conditions. The behavioral clustering approach achieved excellent overall performance with a mean accuracy of 0.74, outperforming localized models (0.71) and unified models (0.67). Behavioral clustering demonstrated effectiveness in 66% of wells, with individual accuracy improvements reaching up to 0.23, while reducing model complexity from five group-specific systems to three behaviorally coherent clusters. Localized models achieved optimal performance in 29% of wells where hydrogeological conditions aligned with spatial assumptions, whereas unified models provided consistent moderate performance across 89% of locations. The incorporation of lagged variables and seasonal indices in behavioral clustering models proved essential for capturing temporal complexity in semi-arid groundwater responses. Statistical analysis revealed lower intra-group variability in behavioral clusters (standard deviation 0.06–0.09) than in geographical groupings (0.08–0.14), confirming improved functional homogeneity through response-based organization. These findings indicate that fuzzy modeling strategy selection should be context-dependent, with behavioral clustering offering an effective balance between accuracy, interpretability, and generalization for regional groundwater management applications. The novelty of this work lies in isolating the effect of fuzzy system organization logic (localized, unified, and behavioral) on forecasting performance, robustness, and transferability, evaluated under an identical inference and time-series validation framework. Full article

Climate-driven extremes in temperature and precipitation are increasingly threatening the stability and serviceability of slopes, embankments, levees, transportation corridors, and other earthen infrastructures founded on expansive and problematic soils. Conventional stabilization strategies, which often treat reinforcement and drainage as separate design elements, struggle to cope with cyclic wetting-drying, freeze-thaw, and prolonged rainfall events that drive desiccation cracking, loss of matric suction, elevated pore-water pressures, and progressive strength degradation. This paper presents a state-of-the-art review of geosynthetic-reinforced slopes with particular emphasis on geogrid geotextile composite systems and their performance under high-temperature, high-rainfall, and low-temperature environments. We first summarize the fundamentals of geosynthetic types, functions, and material properties, then examine how thermal and hydrological processes such as creep, oxidation, frost heave, infiltration, suction loss, and pore-pressure build-up govern the performance of geosynthetic-reinforced soil (GRS) systems. Next, we synthesize recent advances in composite geosynthetics that integrate reinforcement, filtration, separation, and drainage, highlighting laboratory studies, centrifuge modeling, numerical analyses, and field case histories for mechanically stabilized earth walls, pavements, railway embankments, levee systems, and rainfall-induced and expansive soil slopes. Across these applications, geogrid geotextile composites consistently improve hydraulic control, maintain effective stress, and enhance factors of safety under extreme climatic loading. The review concludes by identifying critical research gaps, including coupled thermo-hydro-mechanical characterization, performance-based design approaches, and climate-resilient guidelines for geosynthetic selection and detailing. These findings underscore the potential of composite geosynthetics to enable more sustainable and resilient slope and earthwork infrastructure in a changing climate. Full article

Frequent rainfall during the ripening season in Shaanxi’s grape-growing regions increases the incidence of downy mildew and black rot. In recent years, rain-shelter cultivation has reduced the incidence of these diseases; however, it has been associated with frequent powdery mildew outbreaks that severely compromise fruit quality and yield. To mitigate powdery mildew under rain-shelter conditions, we characterized disease dynamics and evaluated “bio-based” or “microbial-derived” pesticide control strategies. A large number of studies have shown that rain shelter cultivation can significantly change the microclimate. This study found that changes in microclimate affect the incidence pattern of powdery mildew, and there are significant differences in the resistance of different grape varieties to powdery mildew. A prediction model based on microclimate showed that 15-day accumulated growing degree days (GDD15; base 10 °C) before disease onset were positively correlated with the disease index (r = 0.860), whereas relative humidity was negatively correlated (r = −0.637); a multiple regression including both variables explained 81.4% of the variance. In biopesticide screening, blasticidin S and polyoxin inhibited spore germination by >95%. In-shelter efficacy varied among cultivars, and biopesticide effects on fruit quality were also cultivar dependent. For example, blasticidin S increased total phenol and anthocyanin contents in Cabernet Sauvignon but reduced phenolic accumulation in Chardonnay. Full article

Accurate prediction of slope displacement is an important prerequisite for building an effective geological hazard early warning system for disaster prevention and reduction. However, the inherent nonlinearity and time-varying characteristics of slope displacement evolution greatly affect the prediction accuracy. To improve the slope displacement prediction accuracy, a multi-modal data-driven Bayesian-optimized Convolutional Neural Network and Long Short-Term Memory (Bayes-CNN-LSTM) model was constructed. The performance of the model was evaluated using multi-modal monitoring data from the GuShan mine slope. Experimental results showed that the Bayes-CNN-LSTM model achieved an average coefficient of determination (R²) of 0.971, with a mean absolute error (MAE) of 0.444 mm and a root mean square error (RMSE) of 0.618 mm. Compared with the CNN-LSTM, LSTM, CNN, SVM, TCN, and Transformer models, the MAE of the constructed model was decreased by 25.1%, 31.3%, 32.3%, 24.1%, 24.7%, and 17.7%, respectively, and the RMSE decreased by 20.1%, 26.9%, 29.5%, 18.0%, 20.7%, and 12.4%, respectively. Furthermore, the proper integration of multi-modal data can effectively improve the prediction accuracy when extrapolating slope displacement. Based on rainfall and earth pressure data, the average MAE and RMSE of extrapolation (24-h) prediction using the constructed model were decreased by 30.2% and 24.6%, respectively. The model effectively improves the accuracy of slope displacement prediction and enhances the practicality of the slope safety monitoring system, providing valuable reference for slope safety monitoring. Full article

Effective mitigation of urban flash floods under extreme rainfalls requires integrated hydrologic monitoring and rapid response mechanisms. The study presents an adaptive flood response framework. It combines real-time rainfall forecasting, CCTV-based flood image classification, drainage network water level monitoring, pumping machinery operations, and automated response controls. The adaptive strategy is structured into three phases to support real-time decision-making: (1) atmospheric sensing and pre-alert actions, (2) subsurface drainage system monitoring and alert activation, and (3) surface run-off detection and response. Over three years of implementation in New Taipei City, the adapted strategy achieved an over 80% success rate in preventing street inundation during intense rainfall events (>25 mm per 10 min). By integrating ensemble modeling, remote sensing, and decision-support tools, the platform transforms climate-induced flood risks into opportunities for resilience. Full article