Search Results (6,401)

Green infrastructure (GI) provides essential ecosystem services for urban sustainability in the face of urbanization and climate change, including stormwater management, heat mitigation, and reduction in carbon dioxide (CO₂) concentration levels. Existing studies often focus on single-dimensional ecological effects, lacking a systematic investigation of their synergies and trade-offs. This study developed a coupled framework integrating scenario design, model simulation, and multi-indicator evaluation. Fifty-six scenarios, varying by GI combinations, weather conditions, and total annual runoff control rate (RCR), were applied to a high-density industrial district in Nanjing. The results showed that: (1) GI combinations enhanced comprehensive benefits, with the combination including bioretention (BR), permeable pavement (PP), and green roof (GR) performing most effectively. This was followed by the combination of BR and PP, then by BR and GR, while the use of BR alone provided the lowest effectiveness. (2) PP was a key synergistic component, improving heat mitigation and reducing CO₂ concentration levels through the beneficial effects of rainfall events. (3) Exceeding the optimal RCR threshold for some GI combinations diminished tree space and three-dimensional green volume, shifting synergies into trade-offs. (4) Three-dimensional green volume was positively correlated with reductions in Physiological Equivalent Temperature (PET) and CO₂ concentration, confirming its core role. (5) Rainfall boosted carbon sinks, while a significant cooling enhancement required PP. This study elucidates the water–heat–carbon synergy in small-scale GI, supporting multi-objective optimization in high-density urban renewal. 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

Per- and Polyfluoroalkyl substances (PFASs) are commonly detected in airport stormwater runoff due to historical and ongoing use of aqueous film-forming foams (AFFFs). Conventional stormwater control measures (SCMs) are generally effective at removing PFASs associated with the particulate fraction, but may provide limited removal of dissolved-phase PFASs. Sorbent polishing beds represent a potential downstream treatment option; however, their applicability and performance for PFASs in stormwater have not been well studied. In this study, measured PFAS concentrations and runoff volumes from an AFFF-affected airport apron were combined with literature-derived sorption parameters to develop a screening-level framework for evaluating adsorber beds as polishing units for SCM effluent. Bed sizing was calculated using a representative empty bed contact time (EBCT) of 10 min and a design volume based on the 85th percentile storm event. Sorbent performance was evaluated using literature equilibrium partition coefficients (K_d) for activated carbons, ion exchange resins, and specialty materials to estimate operational lifetimes prior to regeneration or replacement. Model-based results indicated lifetimes ranging from approximately 7 years for activated carbon to more than 50 years for specialty materials, depending on PFAS chain length and affinity. Sensitivity analysis using quartile K_d ranges showed predicted lifetimes spanning orders of magnitude, emphasizing the screening-level nature of the estimates. This work links field monitoring data with conceptual adsorber design to support early-stage evaluation of sorbent polishing strategies for airport runoff management, supporting compliance under tightening discharge regulations. Full article

Mexico City (CDMX) is located in an endorheic basin historically prone to flooding and waterlogging, the recurrence and magnitude of which have intensified in recent decades. However, flood risk assessment tends to focus primarily on the occurrence of intense rainfall to explain this phenomenon. The main objective of this study is to demonstrate that the risk of flooding in Mexico City (CDMX) depends not only on intense rainfall, but also on changes in hydrological vulnerability resulting from the loss of natural vegetation cover. The curve number (CN) method is used to determine hydrological vulnerability and flood risk in CDMX, integrating environmental information and precipitation values. Changes in surface runoff are also determined for 10 watersheds located west of Mexico City, considering urbanization in 1992 and 2021, as well as a non-urbanized scenario. The results indicate that hydrological vulnerability and flood risk increased from acceptable levels to “high” and “very high” levels, mainly in regions where urbanization increased and natural vegetation decreased. It was also identified that, under different levels of precipitation, agricultural and urban land cover have considerably lower infiltration capacities compared to natural land cover, such as forests, which infiltrate more than half of the precipitation. Finally, the increase in surface runoff in the watersheds located west of the city is closely related to the urbanization process and the physical characteristics of the territory. It was also observed that a degraded watershed can generate approximately 90% more runoff than a non-urbanized watershed. 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

This study investigates the characteristics of water and sediment evolution under the influence of the Datengxia Water Control Hub Project by analyzing its affected area, with a focus on the driving mechanisms of human activities on these processes. Utilizing hydrological data (1993–2022) from the Wuxuan and Dahuangjiangkou Stations, along with meteorological, land use, and population data, we applied the M–K (Mann–Kendall) trend test, Pettitt change point test, double mass curve method, and a random forest model. These methods were used to quantify the contributions of rainfall and human activities and to identify the dominant controlling factors. Model reliability was verified by comparing predicted and observed P-III (Pearson Type III distribution curve), enabling an assessment of water–sediment changes before and after the project’s construction. The results indicate that (1) both stations showed a non-significant declining trend in runoff and sediment load, with a human activity-induced change point detected in 2003; (2) human activities accounted for 93.18% and 92.38% of the reduction in runoff and sediment load at Wuxuan Station, and 74.44% and 54.33% at Dahuangjiangkou Station, respectively; (3) population density was the dominant factor for water–sediment changes at Wuxuan Station (influence weight: 0.41), while grassland area (0.41) and population density (0.40) primarily controlled runoff and sediment changes, respectively, at Dahuangjiangkou Station; (4) following project construction, the trend of the decreasing flood inundation extent with increasing frequency became more pronounced, and sediment deposition was concentrated mainly in the reservoir area and downstream reaches. The study confirms the dominant role of human activities in the basin’s water–sediment dynamics, and the established methodological framework provides a scientific basis for integrated watershed management and ecological conservation. Full article

Accurate quantification of surface runoff is required for the appropriate design of storage structures, irrigation patterns, waterways, erosion control structures, water harvesting projects, and groundwater development schemes. However, the paucity of runoff data in Iraq and Iran is a serious obstacle. The soil conservation service–curve number (SCS–CN) method is applied in conjunction with remote sensing (RS) and geographic information system (GIS) to predict the surface runoff in the Shatt Al-Arab Region. In the present study, the Shatt Al-Arab Region is defined as the drainage areas and lateral sub-basins that contribute direct surface runoff to the main channel between Qurna city and the Arabian Gulf. Rainfall, land use/land cover (LULC), hydrologic soil group (HSG), and slope maps are developed in a GIS platform and processed to produce surface runoff for 35 years (1979–2013). The surface runoff ranges between 163 mm (2008) and 300 mm (1982) with an average of 233 mm yr⁻¹. The average annual surface runoff in the study area is 33.657 km³. A scatter plot constructed to visualize the connection between annual rainfall and annual runoff reveals a significant positive relation (coefficient of determination (r²) = 0.67, probability value (p) < 0.05). The runoff potential is low in the southern parts of the study area and gradually rises towards the northern parts. Cross-validation of the modeled annual runoff with the annual runoff data shows reasonably close matches (r² = 0.73, p < 0.001) demonstrating the procedure’s suitability. 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

Pulse crops, having the capacity for biological nitrogen (N) fixation, rarely receive N fertilizers, but information is scarce on N balance for pulse crops or pulse crop-spring wheat (Triticum aestivum L.) rotations. The objective of the study was to evaluate N balance based on N inputs and outputs and soil N sequestration rate for pulse crops and pulse crop-spring wheat rotations from 2021 to 2024 in the US northern Great Plains. Pulse crops (chickpea [Cicer arietinum L.], lentil [Lens culinaris Medik.], and pea [Pisum sativum L.]) were rotated with spring wheat to form four crop rotations (chickpea–spring wheat, lentil-spring wheat, pea–spring wheat, and spring wheat–spring wheat). Total N input from N fertilization, biological N fixation, soil N mineralization, crop seed, and precipitation was 9–27% greater for pea than for other crops and greater for pea–spring wheat than chickpea–spring wheat and continuous spring wheat. Total N output from grain N removal, ammonia volatilization, denitrification, plant senescence, leaching, surface runoff, and gaseous emissions was 20–62% greater for spring wheat than pulse crops. Nitrogen sequestration rate at 0–15 cm was 89% greater for spring wheat than lentil and 106–107% greater for pea-spring wheat and spring wheat–spring wheat than lentil–spring wheat. Nitrogen balance was 215–356% greater for chickpea and pea than lentil and spring wheat and 114–118% greater for chickpea–spring wheat and pea–spring wheat than lentil–spring wheat. Greater N input increased N surplus for pea or pea-spring wheat, and greater N output increased N deficit for spring wheat or spring-spring wheat compared to lentil or lentil–spring wheat, indicating that pea alone or in rotation with spring wheat reduced N loss to the environment by increasing soil N storage compared to continuous spring wheat. Full article

Accurate runoff estimation in ungauged basins is critical for water resource management but often relies on static parameters like the runoff threshold (P₀), derived from the Soil Conservation Service Curve Number method, which fail to capture spatiotemporal soil moisture variability. This study proposes an automated methodology utilising Google Earth Engine to dynamically adjust P₀ by integrating daily soil moisture data from SMAP L4, land cover from MODIS, and precipitation from GSMaP. Unlike traditional approaches that use antecedent precipitation as a proxy, this method classifies moisture conditions using historical percentiles to update the threshold daily. The methodology was validated in two sub-basins within the Guadiana River basin (Spain). The results highlight a stark contrast between methods: while static regulatory values remained invariant (36 and 48 mm), the proposed dynamic model revealed significant fluctuations, with P₀ values ranging from over 50 mm in dry periods down to less than 14 mm during saturation. Conversely, the proposed dynamic method effectively captures real-time soil saturation, exhibiting adaptability with reductions in P₀ of up to 72% immediately following rainfall events. This satellite-based approach provides a scalable, physically consistent alternative for assessing runoff potential in data-scarce regions, significantly enhancing the reliability of hydrological modelling compared to conventional regulatory standards. Full article

As urbanization expands, the loss of pervious surfaces has led to greater stormwater runoff and contributed to an increase in urban flooding—localized flooding in areas not formally designated as flood zones. This study evaluates the potential of decentralized active rainwater harvesting (RWH) to mitigate urban flooding in semi-arid urban environments. A neighborhood in northeast El Paso, Texas, was selected as a pilot site. Using a GIS-HEC-HMS modeling framework, approximately 9000 residential parcels were analyzed to assess rooftop harvesting capacity, runoff potential, and system feasibility under different adoption rates and antecedent moisture conditions. Land cover and building footprints were extracted using supervised machine learning to generate stormwater runoff parameters and catchment areas for rainfall-runoff simulations for storms with return periods ranging from 1 to 50 years. The results indicate that for 1- and 2-year storms, a 25% adoption rate may reduce street runoff by 16–19% from 13.1 to 10.6 × 10³ m³ and from 31 to 26.1 × 10³ m³. Increasing adoption to 50% yields substantially greater reductions of approximately 30–36%. Even higher-magnitude storms (5- and 10-year events) experience measurable decreases in runoff volume, with reductions of 10% for the 5-year storms and up to 10.4% for the 10-year storm at the 25% adoption and 20–22% across the same events at 50% adoption. Overall, the results of this study demonstrate that GIS and HEC-HMS are effective tools for evaluating urban flood mitigation strategies, and that decentralized RWH offers a viable method for reducing flood risk in urbanized settings when adoption levels and storage capacities are considered. 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

The rapid expansion of impervious surfaces in urban environments has significantly increased surface runoff and flood risk. Detention basins, implemented as part of Sustainable Urban Drainage Systems (SUDSs), are widely adopted worldwide to control peak discharges and mitigate recurrent flooding. In this study, an explicit flood routing model is applied to simulate the hydraulic behaviour of an urban detention reservoir, offering a computationally efficient alternative to traditional implicit numerical schemes by avoiding iterative solution procedures. In parallel, twenty-eight machine learning (ML) models are evaluated to estimate the percentage reduction in peak discharge required to comply with local regulatory constraints. The proposed framework integrates explicit hydrological routing with data-driven modelling to support decision-making during the design of detention systems. The methodology is applied to an urban catchment in Cartagena, Colombia, comparing an uncontrolled inflow hydrograph (without SUDSs) with an attenuated outflow hydrograph produced by the detention basin. The results demonstrate a substantial reduction in peak discharge and a delay in the time to peak, fully complying with Colombian regulations that require a minimum attenuation of 30%. Among the evaluated ML models, Squared Exponential Gaussian Process Regression achieved the best performance, yielding coefficient of determination (R²) values of 0.999 in both the validation and test sets. The findings confirm the potential of machine learning techniques to quantify peak-flow reduction requirements accurately and to support the planning and design of detention reservoirs in urban environments. The proposed approach constitutes a practical, efficient, and replicable tool for sustainable urban drainage design since the results of this research can be used to design detention pond systems employing ML tools. Full article

Despite reductions in sulphur and nitrogen emissions, lakes and streams in Europe and North America have shown only partial recovery from acidification. This study aims to assess the chemical and biological recovery of the upper stretch of the Litavka River, currently on of the most acidic stream in the Czech Republic. Water composition and macroinvertebrates were studied for 1999, 2010, and 2021, along with long-term data on hydrology and climate. Over these 22 years, concentrations of SO₄²⁻, base cations, conductivity, and toxic Al forms (Al_i) significantly decreased, but pH only increased from 4.2 to 4.3. Biological recovery was most evident during 1999–2010, with an increase in the number of taxa and the appearance of less acid-tolerant taxa such as stonefly Diura bicaudata and caddisfly Rhyacophila sp., mainly associated with decreased Al_i toxicity. Subsequently, however, despite continued chemical improvement, macroinvertebrate diversity decreased, and sensitive taxa were again absent in 2021. Average annual temperature increased by 2.4 °C over the past 50 years (1970–2020) while precipitation remained unchanged, resulting in significant aridification of the regional climate. We attribute the lack of biological recovery in 2021 to climate-related changes, including more frequent dry periods and floods. Although partial biological recovery of the river followed chemical recovery, the increasing frequency of hydrological extremes has likely become the main limiting factor. Full article

This study evaluates the real-world performance of a TiO₂ compound parabolic collector (CPC) photocatalytic reactor operated under natural sunlight for the treatment of agricultural runoff. The three objectives are to determine whether photocatalytic performance can be reliably predicted using a spectrally relevant UVA dose, quantify the impact of water-matrix optical attenuation on degradation efficiency, and lastly, to assess whether an adaptive irradiance-gated control strategy can improve operational throughput. Field Analytical Models are conducted by using a 5 L recirculating CPC slurry reactor treating three model agro-pollutants under mid-latitude outdoor conditions. Kinetics followed pseudo-first-order behaviour when analysed against cumulative UVA dose, which reduced inter-day variability in apparent rate constants from more than 30% (time-based analysis) to less than 10%. Natural river water shows a 20–35% reduction in removal efficiency relative to synthetic runoff, which was correlated with lower UV transmittance and higher UV254 absorbance. Catalyst reusability tests indicated only an 18% loss of activity after five cycles, with partial recovery after rinsing. Importantly, the proposed adaptive UVA dose control increased the daily treated volume by 25–35% compared with continuous operation. These results demonstrate that solar photocatalysis can be transformed into a predictable, optimisable treatment process when spectral irradiance, matrix optics, and intelligent operation are considered together. Full article