Search Results (1,631)

Stormwater runoff represents a significant vector for the transport of organic pollutants and pathogens into aquatic ecosystems, posing serious environmental and public health risks. Although extensively employed for bank stabilization, traditional gabion structures demonstrate constrained efficacy in pollutant removal. In this study, an enhanced ecological gabion (EG) system was developed by integrating a stratified configuration of functional fillers (ceramsite, maifanite, and biochar) with vegetation (Iris germanica). This design leverages synergistic effects to enhance the concurrent removal of dissolved organic matter (DOM), particulate organic matter (POM), and fecal indicator bacteria (FIB) from simulated stormwater. The system was evaluated in continuous flow experiments through comparison with a traditional gravel gabion (TG). Results showed that, compared with the TG, the EG exhibited markedly enhanced removal performance, with chemical oxygen demand (COD), NH₄⁺–N, and TN removal efficiencies being approximately 2.48, 3.68, and 3.56 times those of the TG, respectively. In addition, the EG exhibited significantly higher removal efficiencies for both particulate organic carbon (POC) and dissolved organic carbon (DOC) than the TG, with increases of 329% and 137%, respectively. Fluorescence spectroscopy and particle size distribution analyses revealed that the EG effectively transformed and removed diverse DOM components and fine particulates. The stratified filler media synergistically enhanced pollutant retention, with biochar serving as the primary agent for nutrient and pathogen adsorption. These findings demonstrate the viability of the EG as an integrated, eco-friendly solution for enhanced stormwater purification in riparian zones, providing a compact and multifunctional alternative to conventional end-of-pipe systems. Full article

The application of computational simulation in industrial engineering plays a critical role in designing sustainable infrastructure solutions. We applied a hexagonal green pavement system developed through digital simulation to address challenges in urban stormwater management. The system comprises an upper base layer that bears structural loads and a lower support layer designed for water infiltration and drainage. Structural performance was evaluated using SolidWorks simulations under static loads of up to 1100 N. The results indicate that stress values remain within the material’s yield strength, ensuring structural reliability. Hydraulic performance was also assessed using various valve opening scenarios to simulate different rainfall intensities. The system demonstrated effective infiltration capability, with flow retardation coefficients ranging from 0.66 to 0.80. These findings validate the system’s potential to reduce surface runoff and mitigate urban flooding. The study results highlight how digital simulation, as part of a digital twin framework, can support the development of resilient, modular infrastructure for sustainable urban drainage. This approach represents a practical application of industrial engineering computation to advance smart and eco-friendly urban systems. Full article

Rapid land-cover transformations associated with urban expansion have increasingly altered hydrological processes, modifying runoff generation and flood response at the catchment scale. This study applied the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) to examine rainfall–runoff dynamics in the Prosna River catchment (Poland) and the Morava e Binçës River catchment (Kosovo) for 2006–2021. Land-use changes were quantified using CORINE Land Cover (CLC) data from 2006, 2012, and 2018, and their hydrological effects were evaluated through changes in the Curve Number (CN) parameter. The model was calibrated and validated for the Prosna catchment, achieving satisfactory performance (NSE = 0.72 during calibration and 0.56 during validation), confirming its reliability under varying hydrometeorological conditions. Due to the lack of continuous discharge data in Kosovo, a parameter-transfer approach was used, applying calibrated parameters from the Prosna to the Morava e Binçës. Scenario-based simulations assessed the combined effects of urban growth and meteorological variability. Under wetter conditions, increased precipitation and expanded impervious surfaces markedly amplified simulated discharge, with maximum daily differences reaching 86.9 m³ s⁻¹. These findings underscore the sensitivity of catchment response to interacting land-use and precipitation changes and highlight the need for improved hydrological monitoring in data-scarce regions. Full article

The increasing frequency and intensity of extreme climate events have posed more geohazards worldwide. It is therefore crucial to quantify and map risk to reduce disaster-related losses. The main objective of this study is to propose a quantitative framework to conduct risk assessment of buildings and infrastructures impacted by geohazards. A debris flow hazard in Tianjin, North China was taken as a case study. A physically based model and the Gumbel extreme value distribution were utilized to construct a range of extreme rainfall and runoff scenarios. The FLO-2D and ABAQUS software were subsequently employed to simulate the surging behavior of the debris flow and assess the structural vulnerability of buildings, respectively. Furthermore, the number of elements at risk and economic values were estimated to generate risk maps. The results revealed that variations in peak discharge in the channel evidently affected flow velocity and depth, thus elevating the debris flow intensity and the likelihood of the materials threatening buildings. The stiffness degradation of concrete was strategically used as the indicator to quantify structure vulnerability and effectively present the dynamic responses under the impacts of the debris flow. Under a 100-year return period rainfall scenario, the proportion of very high- and high-risk areas reached 31%, with the estimated economic loss approximately ¥167.7 million. This highlighted the critical role that extreme rainfall played in shaping both the spatial distribution and severity of debris flow risks. The proposed method provides a scientific basis for enhancing the resilience of mountainous regions to compound natural disasters exacerbated by climate change. Full article

Flood events are major drivers of soil erosion and sediment yield on the Loess Plateau, where extensive ecological restoration has been implemented. This study investigates runoff–sediment dynamics by analyzing 215 flood events recorded in the Chabagou watershed (1959–2022), with a focus on changes under intensifying restoration efforts. Using long-term hydrological and rainfall data, we applied cluster and discriminant analyses to classify flood events based on sediment hysteresis loops and evaluated variations across three management periods (1959–1979, 1980–1999, and 2000–2022), characterized by progressive increases in check dam construction and vegetation recovery. The results show that the floods characterized by short duration, low peak flow, and low sediment concentration were predominant, accounting for 77.7% of the recorded 215 events. A clear decreasing trend was observed, with average sediment yield and peak discharge declining by approximately 68% and 52%, respectively. Anticlockwise hysteresis loops were most common (45.6%), followed by complex (27.9%) and figure-of-eight loops (23.7%). The proportion of figure-of-eight loops increased notably from 17% to 39%, indicating reduced sediment connectivity due to large-scale ecological restoration. Extreme rainfall events consistently produced complex hysteresis patterns, influenced mainly by rainfall intensity but increasingly modulated by human interventions. These results highlight adaptive watershed management strategies that target figure-of-eight and complex flood events to mitigate erosion and flood risks. Full article

Urban flooding represents an increasingly critical challenge in rapidly urbanizing cities, where high-density development and climate variability intensify hydrological vulnerability. This article presents an analytically focused case study of Shenzhen, a national Sponge City pilot, to examine not only whether nature-based interventions are associated with flood-resilience gains but also under what spatial, institutional, and governance conditions such gains emerge. The study adopts a qualitative mixed-methods case-study design based on secondary sources, integrating observed flood-event records, reported hydrological and water-quality indicators, model-based projections, and systematic policy analysis. Drawing on data from 2006–2020, the analysis explicitly distinguishes observed outcomes, reported performance indicators, and inferred effects, addressing a key methodological limitation in existing Sponge City assessments. Results indicate that, within designated pilot zones, Sponge City interventions are associated with reduced surface runoff, attenuated peak flows, and reported improvements in pollutant filtration, particularly where green infrastructure density and monitoring capacity are high. However, these performance patterns are spatially uneven and mediated by governance constraints, including institutional fragmentation and maintenance capacity. The principal contribution of the study lies in identifying governance–infrastructure mechanisms that condition Sponge City performance and scalability. By treating Shenzhen as a critical rather than representative case, the article offers analytically transferable insights into the effectiveness, durability, and limits of nature-based flood-management strategies in high-capacity urban contexts. Full article

As China’s Sponge City Program (SCP) shifts towards retrofitting old communities, enhancing flood resilience is critical for sustainable urban renewal. However, engineering practice often encounters a performance inversion—characterized by high design evaluation scores but low operational efficiency. This issue largely stems from relying on the static importance of indicators while neglecting dynamic driving forces within the socio-technical system. To address this, this study aims to construct a Robust Causal Diagnostic Framework integrating Improved AHP, DEMATEL, and K-means clustering. Through a quadrant positioning and cluster locking mechanism, it identifies Hidden Leverage Factors (HLFs)—critical indicators typically assigned low weights but exerting strong driving forces. To demonstrate the practical application of this framework, an empirical analysis of H City’s DG Community was conducted, identifying residents’ willingness and design pertinence as the project’s HLFs. Optimization strategies based on this diagnosis were simulated using SWMM. Results show that the Annual Runoff Volume Capture Ratio increased by 44.45%, with significant improvements in peak flow reduction and water purification. This study facilitates a shift from empirical evaluation to precision diagnosis, offering a quantitative reference for enhancing urban flood resilience under complex social constraints. Full article

To address the limitations of traditional runoff prediction methods—namely, the oversimplification of meteorological factor selection, ambiguous interactions among core variables, and the disruptive influence of redundant inputs—this study focuses on the Zijiang River Basin as a representative case. A suite of machine learning models, including Long Short-Term Memory Neural Network (LSTM), Convolutional Neural Network (CNN)-LSTM, Temporal Convolutional Network (TCN), and Gradient Boosting Regression Tree (GBRT), was constructed and trained using 13 distinct combinations of meteorological variables. These configurations were systematically evaluated to assess their compatibility with each model in simulating daily runoff patterns. Additionally, the Shapley Additive Explanations (SHAP) algorithm was employed to quantitatively assess the contribution of each factor to predictive accuracy. Among the models tested, the TCN model consistently demonstrated superior performance, particularly in mitigating the effects of irrelevant or redundant features. The GBRT model showed distinctive strengths in accurately predicting peak flow timings. Of all input configurations, the combination of “runoff + precipitation + evaporation + temperature” emerged as the most effective. Findings indicate that the predictive value of individual meteorological variables hinges primarily on their direct correlation with runoff, while the effectiveness of multi-factor schemes depends on the degree of functional integration—specifically, the coupling of hydrological recharge, consumption, and regulatory processes. The presence of redundant variables was found to impair model performance unless they contributed to a meaningful synergistic relationship with core inputs. The SHAP analysis further reinforced these insights: precipitation-related variables proved to be the most critical to prediction accuracy, whereas temperature and evaporation served more complementary roles. Notably, the inclusion of relative humidity tended to suppress runoff responses and increased deviation in peak timing estimates. These findings shed light on the nuanced interplay between meteorological input design and model selection, offering a robust foundation for optimizing data-driven runoff prediction frameworks. Full article

Soil tillage practices play a key role in controlling soil’s physical properties, water infiltration, and runoff generation, particularly in erosion-prone cropping systems such as maize monocultures. The cultivation of wide-row crops is restricted on erosion-prone land; however, these crops constitute a fundamental basis for livestock feed and represent a key input raw material for biogas plants. This 4-year study evaluated the effects of three tillage practices—conventional ploughing, shallow tillage, and no tillage—on selected soil’s physical and chemical properties and on water infiltration processes in a maize (Zea mays L.) monoculture. Experimental maize stands were established in a field with a silty clay Luvic Chernozem. Field measurements were performed over multiple years and included soil bulk density, macroporosity, cone index, soil organic carbon, soil pH, soil aggregate stability, and water infiltration. Infiltration processes were assessed using a combination of double-ring infiltrometers, rainfall simulation, and dye tracer experiments to characterize water flow patterns under controlled conditions. The results demonstrated that soil tillage significantly influenced the vertical distribution of soil organic carbon and pH, soil aggregate stability, soil compaction, and pore characteristics, with the most pronounced differences observed in the upper soil layers. Soil aggregate stability in the 0–0.10 m layer showed a clear numerical trend, with the highest mean value under ST (0.42) compared with PL (0.28) and no tillage (NT) (0.26). Topsoil Cox was the highest under ST (3.591%) compared with PL (2.838%) and NT (2.634%). Differences among tillage practices were particularly evident during simulated rainfall events, affecting infiltration rates, runoff initiation, and preferential flow patterns. Ring infiltrometer measurements indicated higher infiltration in PL (e.g., 21.1 mm min⁻¹ at minute 1 in PL vs. 11.1/11.9 mm min⁻¹ in ST/NT; 10.9 mm min⁻¹ at minute 10 in PL vs. 5.3/7.6 mm min⁻¹ in ST/NT). However, rainfall simulation showed the highest runoff in PL, including the earliest runoff onset (4.5 min). Despite the soil’s high infiltration capacity due to low bulk density and higher porosity, the decisive factor promoting water infiltration into the soil is the condition of the soil surface, which is influenced by the stability of soil aggregates; this stability was enhanced by the input of organic matter from plant residues. The findings confirm that long-term soil tillage management substantially modifies soil hydraulic behaviour and highlight the importance of tillage system selection for improving soil water infiltration and reducing runoff risk in maize monoculture systems. Full article

To clarify the differences in and mechanisms of soil detachment before and after soil collapse, five typical granite soil layers (red soil, red soil–sandy soil, sandy soil, sandy soil–debris, and debris layers) of Benggang in Anxi County, Fujian Province, were studied via laboratory runoff scouring tests, and the detachment capabilities and influencing factors of undisturbed (original) and disturbed (colluvial deposit) soils were compared. The results showed that disturbance due to soil collapse significantly increases the soil detachment capacity by an average of 1046 times, with the greatest increase occurring in the red soil–sand soil layer (3494 times) and the smallest increase occurring in the debris layer (63 times). The undisturbed soil detachment capacity increases with increasing soil depth, whereas the disturbed soil capacity first increases but then decreases, with the sand layer having the highest capacity. Hydrodynamic fitting results revealed that undisturbed red soil has a linear relationship, red soil–sandy soil and sandy soil layers have power function relationships, and sandy soil–debris and debris layers have logarithmic relationships with flow shear stress. Disturbed red soil and red soil–sandy soil layers are linearly related, whereas the other layers are logarithmically related. Correlation analysis revealed that undisturbed soil detachment is significantly negatively correlated with clay, silt, gravel, free iron oxide, and free alumina contents and positively correlated with sand content. Disturbed soil shows similar correlations, but it has a negative correlation with organic matter instead of gravel. Structural equation modelling (SEM) path analysis revealed that undisturbed soil detachment is affected mainly by negative free alumina oxide content (path coefficient of −0.87) and flow shear stress (path coefficient of 0.14), whereas disturbed soil is controlled mainly by negative shear strength (path coefficient of −0.76) and positive flow shear stress (path coefficient of 0.49). This study elucidates the mechanism by which colluvial deposit disturbance accelerates soil detachment, providing a theoretical basis for the prevention and control of Benggang erosion in the hilly regions of southern China with red soil. Moreover, the comparative research strategy adopted in this study offers a reference for related investigations in similar erosion-prone areas. Full article

Non-point source pollution has become one of the most widespread environmental degradation problems in recent years. This study aimed to investigate how hydrological processes regulate nitrogen and phosphorus losses under simulated rainfall conditions through in situ rainfall experiments in karst farmland. We conducted a field-scale plot experiment, recorded rainfall and runoff, and measured the nutrient concentration in the runoff of nine experimental plots on the slope toe, middle slope and upper slope. Simulated rainfall intensity was 90 mm/h for 60 min. The results showed nitrogen losses were dominated by subsurface flow in small-scale studies, which accounted for 55.19% (2.50 kg/ha), 71.35% (3.88 kg/ha), and 93.85% (1.39 kg/ha) of TN losses at the toe, middle, and upper slope positions, respectively. The middle slope exhibited the highest losses of N mainly due to its larger subsurface runoff volume. NH⁴⁺ dominated TN in surface flow, contributing up to 97.5% (0.0092 kg/ha) at the slope toe, whereas NO³⁻ was the dominant N form in subsurface flow, with little variation across the three slope positions, averaging 0.062 kg/ha. In contrast, phosphorus losses are primarily associated with surface flow, with TP concentrations in surface flow being 5–60 times higher than those in subsurface flow, with average surface TP losses of approximately 0.04 kg/ha. These results imply that nutrient management in karst farmland should adopt differentiated control strategies, with greater emphasis on reducing subsurface nitrogen leaching while limiting surface runoff and erosion to mitigate phosphorus losses. However, the conclusions are based solely on small-scale rainfall simulation experiments, and nutrient loss may also be influenced by factors such as karst terrain heterogeneity, prior soil moisture content, soil properties, and rainfall characteristics. Full article

Fertilization plays an important role in soil nutrient loss from sloping croplands. However, the effect of fertilization on Molybdenum (Mo) loss remains unknown. The aims of this study were to explore the effects of different fertilizers of purple soil on the characteristics of soil molybdenum loss in surface, subsurface runoff and sediments. Five fertilizers treatments (3 replicates) were designed as following: no fertilizer (CK); conventional nitrogen, phosphorus, and potassium fertilizer (NPK); organic fertilizers with livestock manure (OM); nitrogen, phosphorus, and potassium fertilizer plus organic fertilizers with livestock manure (OMNPK); and straw turnover plus nitrogen, phosphorus, and potassium fertilizer (RSDNPK). The changes of runoff-related Molybdenum loss from June to September 2025 were studied. Results showed that fertilization significantly reduced surface runoff and sediment yield compared with CK (p < 0.05). The RSDNPK treatment exhibited the lowest surface runoff, while OM and OMNPK treatments most effectively decreased sediment loss. Dissolved Mo (DMo) was the predominant form of Mo loss across all treatments (50~70% of total loss), significantly higher than particulate Mo (PMo, 25~40%) and Mo of soil sediments (SEMo, 6.5~12.9%). Notably, the OM treatment uniquely shifted Mo loss toward subsurface flow (47.2% of total), whereas other treatments were dominated by surface runoff. Total Mo loss amount varied significantly among treatments (p < 0.05): CK (795 μg/m²) > OM (685 μg/m²) > NPK (596 μg/m²) > OMNPK (533 μg/m²) > RSDNPK (373 μg/m²). The RSDNPK treatment achieved the optimal performance, reducing total Mo loss by 53.1% compared with CK. Structural equation modeling revealed that soil organic matter indirectly controlled Mo loss by modifying soil physical properties and hydrological processes. The findings demonstrate that RSDNPK represents the most effective strategy for minimizing Mo loss in purple soil sloping croplands, outperforming sole organic manure application. This study highlights the importance of organic amendment and management in Mo loss control and provides a scientific basis for sustainable nutrient management in erosion-prone agricultural systems. Full article

Runoff forecasting is an essential application in the management of water resources and sustainable development. In practice, there are limitations in the forecast results because of factors such as data unavailability, noise interference, and spatiotemporal variation in multi-site data. To overcome the limitations, this paper proposes a hybrid forecast model based on Autoencoder (AE), Sparsified Dynamic Graph Convolution (SDGC), and Autoformer. The AE cleans noise and sharpens feature representation, the SDGC constructs dynamic adjacency matrices via the Multidimensional Dynamic Time Warping (MDTW) and sparsifies with a parameterized Multi-Layer Perceptron (MLP) to capture time-varying spatial correlations among stations, and the Autoformer decomposes features to model long-term nonlinear runoff trends through its autocorrelation mechanism. The experiment was carried out in six locations in the southeastern part of Guizhou province during the wet and dry periods and was contrasted with different mainstream models and supplemented with hydrological mechanism consistency analysis. Experimental results show that the hybrid model performs better than all the other models. In the short-term runoff simulation at XingHua Station during the wet season, NSE attains the maximum value of 0.891, with RMSE decreased by 6.5% to 24.1% and MAE by 20.2% to 35.5%. This model provides accurate runoff data to support flood early warning, dry-season water scheduling, and ecological flow protection, offering a reliable tool for sustainable water resource management in complex karst basins. Full article

Riverbed siltation in estuaries affects ecosystem functioning, water quality, and navigation. This study investigates the flow-regulated Osellino Canal, a freshwater tributary of the Venice Lagoon that crosses a largely urbanized area and is undergoing progressive siltation. High-resolution measurements of discharge (Q) and suspended sediment concentration (SSC) were performed using hydroacoustic instrumentation from September 2019 to December 2021. The analysis examined discharge dynamics, sediment transport, and rainfall-runoff relationships. Results indicate a mean annual discharge of 2.1 m³ s⁻¹ and an average annual suspended sediment load of ~2900 ± 330 t. Discharge patterns were strongly influenced by water management, resulting in anomalous runoff coefficients (δ > 1) during dry periods. Sediment export proved to be strongly event-driven: episodic high-flow events accounted for about 23% of the total load despite representing only a small fraction of the study period. Furthermore, a strong linear relationship between runoff and sediment load (R² = 0.94) confirms an advection-dominated regime, where net export is regulated primarily by hydrodynamic volume rather than fluctuations in sediment supply. Bathymetric comparisons (2011–2019) reveal a mean annual sediment retention of 400 ± 100 t yr⁻¹, corresponding to a trapping efficiency of approximately 12 ± 3% relative to the gross sediment input. These findings, supported by SSL–runoff regression residuals, consistently indicate net sediment accumulation associated with the long-term malfunction of a miter-gate system that impedes efficient sediment export. This study provides a critical pre-rehabilitation baseline, establishing a benchmark to evaluate the effectiveness of ongoing restoration efforts initiated in March 2022 and the future hydromorphological recovery of the canal. Full article

Large-scale river modeling has transitioned toward vector-based routing, yet the global fidelity of standalone frameworks like mizuRoute remains poorly characterized due to fragmented observation networks and unquantified systematic biases. This study addresses this gap by establishing a comprehensive global benchmark using a harmonized database of 12,115 in situ gauging stations integrated with multi-dimensional catchment attributes. Simulations utilize the 5 km MERIT-Hydro network driven by ERA5-Land runoff from 1980 to 2024. Our results reveal a robust global median Pearson correlation of 0.53, though simulation efficiency is highly bifurcated with a median Kling–Gupta Efficiency (KGE) of 0.17. High fidelity is concentrated in humid temperate and cold regions, whereas performance collapses in arid zones (median KGE = −0.15) due to the structural omission of channel transmission losses. Attribution analysis identifies the aridity–moisture gradient and vegetation density as primary drivers of model skill, while topographic complexity is well-preserved by the vector framework. Furthermore, anthropogenic regulation significantly degrades accuracy; in basins with high reservoir density, naturalized routing fails to capture regulated flow signatures, leading to a sharp decline in efficiency. This work provides the first global appraisal of the mizuRoute framework and highlights that integrating dryland-specific loss functions and reservoir modules is essential for the next generation of global hydrological reconstructions. Full article