Search Results (1,384)

Urban pluvial flood analysis requires spatial inundation information within operationally useful computation times. This study develops a graphics processing unit (GPU)-accelerated Node–Edge Urban Flood Model (GNE-UFM) that couples SWMM-style runoff generation, structured-grid surface flow, a typed sewer graph, and conservative surface–sewer exchange. The model was evaluated in the Sillim drainage district using the August 2022 observed event and three one-hour rainfall scenarios of 40, 90, and 150 mm, with inputs and output thresholds matched to InfoWorks ICM. For the synthetic scenarios, GNE-UFM achieved CSI values of 0.878–0.904 and wet-union RMSE values of 0.050–0.078 m, while full coupled GPU runs closed the effective runoff mass balance with absolute residuals no larger than 0.0284%. Runtime was comparable to ICM for the lowest-intensity case and became more efficient for the 90 and 150 mm cases, with GNE-UFM maintaining nearly constant RTR as rainfall intensity and inundated area increased. Full article

Rainfall-driven inlet clogging is a phenomenon where inlets of stormwater networks are covered with debris, fallen leaves, or trash flushed by street runoffs due to heavy rainfall. Surface runoffs are thus unable to enter sewer networks that are still under capacity, leading to greater flood risks. To provide a feasible way to reflect such inlet clogging in pluvial urban inundation modeling, a new event-based reverse computing approach using simulated and surveyed data is proposed. The performance of this approach in the aspect of accuracy improvement is then discovered in a flood-vulnerable area. The optimal way for delineating clogged inlets is first explored. Next, the possible range of clogging factors used to represent capacity-reduced effects in inlets is decided by comparing the simulated and reverse-computed flood volumes. The impact of inlet clogging on urban inundation physics is also assessed. The results indicate that, for the study site, considering inlets surrounded by trees to be 10–40% clogged (median of 25%) will increase overall accuracy. Furthermore, inlet clogging reduces drained discharges entering sewer networks, thereby increasing surface water volumes by about 10–20%, diminishing spurious sewer surcharge, and decreasing sewer water depths. Interestingly, greater drained discharges are found in clogged inlets at local depressions, even though most clogged inlets have smaller drained discharges. In summary, the present approach is demonstrated to be a useful tool to account for rainfall-driven inlet clogging in urban inundation modeling. Full article

Vegetation restoration is widely used to combat soil erosion on the Chinese Loess Plateau, where intense rainfall and steep slopes make this region one of the most eroded areas in the world. However, the effectiveness of strip grass cover (Vc) in reducing erosion under varying rainfall and topographic conditions remains insufficiently quantified. In this research, based on indoor simulated rainfall experiments in a soil tank, we investigated the erosion characteristics of slopes under six Vc levels (0%, 20%, 30%, 40%, 50%, and 60%), three rainfall intensities (RI) (1.33, 1.67, and 2.0 mm·min⁻¹), and four slope gradients (SG) (10°, 15°, 20°, and 25°), and quantified the effects of Vc on slope erosion processes. Runoff and sediment reduction effects by Vc ranged from 3.5% to 62.6% and from 15.2% to 99.1%, respectively. An exponential decay in erosion rate was observed with increasing Vc, whereas runoff velocity initially decreased and then increased as the Vc increased. By integrating RI, SG, and Vc, the average runoff rate and erosion rate models accurately simulate erosion processes on Vc slopes. These findings provide laboratory-based evidence that Vc substantially reduces slope erosion, and support the development of empirical models for predicting runoff and erosion under the tested conditions. Full article

Nitrogen export dynamics in small watersheds of subtropical dry–hot valleys remain poorly characterized. This study investigated runoff nitrogen patterns in a typical mango planting area lacking long-term monitoring by coupling in situ monitoring with the SCS-CN model to optimize parameters and the river-entry coefficient during the research period of 2021–2022. Results showed that runoff nitrogen concentration peaked significantly from May to August (p < 0.05), with NO₃-N as the dominant form, accounting for 51.50~76.56% of total nitrogen. The SCS-CN parameter λ exhibited a highly significant linear correlation with monthly rainfall (p < 0.01), ranging from 0.07 to 0.30. Spatially corrected TN river-entry coefficient (0–0.50) were elevated in riparian zones with steeper slopes. Annual TN loads were 5.902 kg/ha and 5.566 kg/ha, respectively. Overall, May to August constitutes the critical period for nitrogen export in this dry–hot valley, primarily as NO₃-N. Optimizing model parameters enhances the applicability of the SCS-CN model, revealing that nitrogen influx into rivers is concentrated near steep riverbanks, providing a scientific basis for pollution control in sloping orchards. Full article

Runoff from manure-fertilized farmlands is an important pathway for microbial contamination of receiving waters within a One Health framework. Although organic fertilizers improve soil quality, their influence on pathogen release under mixed fertilization remains insufficiently resolved. This study aims to establish a quantitative framework linking fertilization intensity to pathogen export, which is essential for developing risk-informed nutrient management strategies. Therefore, controlled simulated rainfall experiments (60 mm·h⁻¹) were conducted on soil plots receiving three nitrogen-equivalent fertilizer regimes: T1 (100% inorganic fertilizer), T2 (37.5% organic nitrogen replacement), and T3 (50% organic nitrogen replacement). Metagenomic sequencing and qPCR (targeting 16S rRNA, E. coli, Cryptosporidium, etc.) were used to characterize pathogen-marker sources, initial soil pathogen-marker burdens, and runoff dynamics during a 50 min rainfall event. A total of 29 pathogen-marker taxa were detected among the 41 targeted taxa. Higher proportions of organic fertilizer were associated with greater initial pathogen-marker loads in the soil and greater cumulative export in runoff, with T3 consistently exhibiting the highest levels across indicators. Based on the two-pool model developed in this study, the fitted total release potential (Total M) of Escherichia in T3 reached ~2.6 × 10⁹ copies·m⁻², compared with ~1.3 × 10⁹ in T1 and ~6.8 × 10⁸ in T2. Interestingly, the relative ranking between T1 and T2 varied among specific pathogen markers, suggesting a potential environmental buffering capacity at moderate organic substitution levels. Across all treatments, pathogen-marker concentrations peaked during the early stages of rainfall, indicating a pronounced first-flush effect. Furthermore, bacterial indicators were mobilized more readily than fungal and protozoan targets, the latter of which were more strongly retained by the soil matrix. Overall, the results suggest that optimized organic–inorganic fertilization ratios, combined with improved manure stabilization, are essential for mitigating downstream microbial contamination potential within a One Health framework. Full article

Spatial and temporal patterns of terrestrial water storage (TWS), and their relationship with groundwater levels, were investigated with the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Data Assimilation System (GLDAS) land surface model results, and climate observations for the Murray–Darling Basin (MDB). The results show that: (1) TWS displays a clear temporal variability: a negative TWS anomaly with a declining trend during 2002–2009, a positive TWS anomaly with a decreasing trend during 2010–2017, and a period of mixed positive and negative TWS anomalies being accompanied by an increasing trend from 2018 to 2025; (2) five dominant cluster patterns were identified that explain the spatial variability of temporal TWS across the MDB; (3) overall, TWS temporal variability is strongly correlated with rainfall, although it is weak at certain locations; (4) TWS is also influenced by evaporation (both actual and potential evapotranspiration, AET and PET) and runoff, and a combined model significantly improves the overall performance in explaining TWS temporal variability; and (5) TWS-derived groundwater storage changes show both similarities and differences in comparison with groundwater level observation changes, reflecting complex hydrogeological processes and the influence of human activities such as groundwater extraction. These findings provide valuable insights to support improved groundwater resource management with GRACE satellite information and land surface models. Full article

Extreme rainfall events intensify flood-related hazards in mountainous national parks and their surrounding communities, where complex terrain and coupled hazard processes create major challenges for spatial risk management. This study focuses on the Tangjiahe district of the Giant Panda National Park and develops an integrated framework for flood-related multi-hazard identification and zoning. The 100-year flood process was simulated using Hydrologic Engineering Center’s River Analysis System (HEC-RAS), runoff retention was assessed using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and slope stability risk zoning was conducted using the Analytic Hierarchy Process (AHP). Based on multi-source spatial overlay, Integrated Flood-Related Multi-Hazard Risk Zoning was generated. Spatial statistical analyses, including Global Moran’s I, Local Indicators of Spatial Association (LISA), and Getis-Ord Gi*, supported the identification of clustered high-risk areas and hotspot zones. In parallel, Disaster Prevention and Control Zoning was established, classifying the study area into multiple management-oriented zones to support differentiated spatial governance and targeted management. The proposed framework provides a practical approach for integrating multi-hazard processes into spatial planning and disaster risk management in mountainous protected areas. Full article

The Curve Number (CN) remains one of the most widely applied parameters for estimating direct surface runoff. However, its conventional application based on watershed-aggregated tabulated values conceals hydrological variability in regions with contrasting soils and steep topographic gradients. A recurring limitation of distributed CN approaches is the absence of independent hydrological validation; most machine learning models are trained and evaluated against the same SCS-USDA lookup values used to construct the training target, a circular scheme that measures statistical agreement rather than physical credibility. This study develops a reproducible geospatial workflow for distributed CN estimation across continental Ecuador, combining official MAG land use, soil surface texture natural drainage, and topographic slope layers at 1:25,000 scale with a Random Forest regression model at 10 m spatial resolution. The CN reference raster was derived from official geo-pedological layers and independently validated, not against tabulated assumptions, but against observed hydrological behaviour. Field hydraulic characterization across four dominant land cover classes in the Guamote microwatershed (Chimborazo Province), combined with HEC-HMS (US Army Corps of Engineers, Davis, CA, USA) rainfall-runoff modelling over 41 years (1981–2021), confirmed a mean annual discharge of 0.1568 m³ s⁻¹ consistent with the tabulated CN assignments. To our knowledge, this is the first nationally distributed CN map with field-anchored hydrological benchmarking for an Andean country. The Random Forest model achieved an RMSE = 10.4, an R² = 0.42, and an NSE = 0.41, a performance consistent with published field-based CN estimation studies and expected given the inherent scatter of the SCS-USDA method under real-world conditions. Zonal CN comparisons confirmed a mean absolute error below 5 CN units across the Andean highland and Amazon watersheds; the Guamote watershed showed a mean ∆CN below 4 units against the field-calibrated model. Land use and surface texture emerged as the dominant CN predictors, with natural drainage providing critical discrimination in volcanic and poorly drained soil environments. The resulting 10 m national CN map offers a physically grounded, spatially explicit parameterization layer for distributed hydrological modeling and water resources planning across data-scarce Andean and tropical territories, with direct relevance for flood risk screening, irrigation planning, watershed conservation, and climate adaptation under SDG 6, SDG 11, SDG 13 and SDG 15. Full article

Flooding in the Kathmandu Valley has intensified in recent years due to rapid urbanization, unregulated land-use change, and insufficient drainage infrastructure. Existing flood hazard assessments are often based on low-resolution datasets and lack proper field validation. This study presents an integrated flood modeling framework that combines Unmanned Aerial Vehicle (UAV)-derived Digital Elevation Models (DEMs), field-based flood measurements, and hydrological simulations to assess urban flood hazards in the Bagmati-Nakkhu confluence, Nepal. High-resolution UAV-derived DEM and field survey data, including flood marks and high-water levels, were used as the foundation for the analysis. Hydrological modeling was conducted using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC-HMS) to estimate the peak discharges of the Nakkhu River (2000–2024), which were then used to derive design flows for return periods of 5 to 150 years using the Gumbel distribution. These flows were used as boundary condition inputs for the Hydrologic Engineering Center—River Analysis System (HEC-RAS) to simulate flood depth and inundation extent under different scenarios. Flood extents for the 27 September 2024 event were derived from Sentinel-2 imagery and validated against surveyed flood marks. Additionally, land use/land cover (LULC) mapping based on UAV data was used to support flood impact analysis. The results show that flood depths ranged from approximately 0.5 m to 2.8 m, with inundation areas increasing by 35–50% under extreme rainfall. Model validation demonstrated strong agreement with simulated results, with deviations generally within ±0.3–0.5 m. Scenario analysis further indicates that urban expansion significantly increases runoff and flood extent, particularly in low-lying areas near the river confluence. Socio-economic exposure analysis for the 27 September 2024 event indicates that approximately 2569 residents (56.4% of the study zone population) and 4.011 km (77.42%) of the local road network were exposed to inundation. Overall, the results demonstrate that integrating high-resolution UAV data, field observations, and hydrological modeling greatly improves the accuracy and reliability of flood hazard assessments in data-scarce urban environments. Full article

Green roofs are used as nature-based solutions for urban stormwater management and for improving the thermal performance of buildings. Their hydrological performance depends on structural properties and rainfall characteristics, but the relative importance of these factors has not been fully quantified. Therefore, this study aimed to identify the key variables controlling the hydrological effectiveness of a green roof. A conceptual model of a flat roof representing a typical single-family building in south-eastern Poland was developed in the Storm Water Management Model (SWMM), with a modeled roof area of 232 m² and 100% of the roof surface covered by the green roof LID system. A total of 24,576 simulation cases were analyzed, considering different values of soil thickness, berm height, initial saturation, vegetation-related storage, rainfall duration, rainfall probability, and rainfall temporal distribution. The hydrological response was evaluated using peak runoff reduction and cumulative runoff volume ratio determined at selected times after rainfall. Predictive models based on the eXtreme Gradient Boosting (XGBoost) algorithm were developed, and their interpretation was performed using the SHapley Additive exPlanations (SHAP) method. The main novelty of the study is its application-oriented framework combining SWMM simulations, XGBoost modeling, and SHAP explainability to distinguish the factors controlling peak runoff reduction and delayed runoff release from a green roof. The results showed that peak runoff reduction ranged from 10.97% to 100.00%, with a median of 99.91%, indicating a generally high capacity of the analyzed system to attenuate peak flow. In contrast, the cumulative runoff volume ratio increased over time, with median values rising from 0.05% immediately after rainfall to 7.91% after 24 h, confirming the significant retention and detention potential of the green roof. SHAP analysis revealed that peak runoff reduction was governed primarily by berm height, whereas cumulative runoff volume was controlled mainly by initial substrate saturation. The results confirm that different mechanisms control short-term and long-term green roof performance. Full article

The sources of potentially toxic elements (PTEs) in the lower reaches of the Yellow River (LYR) remain poorly understood due to intensive human activities in this region. To elucidate the spatiotemporal distribution characteristics and sources of PTEs, water samples were collected from both mainstream and tributary sites during the dry season (DS) and flood season (FS). Concentrations of eight PTEs (Fe, Mn, Cu, Zn, Pb, As, Cr, and Hg) were determined. The single-factor pollution index, Nemerow comprehensive pollution index, statistical techniques, and the positive matrix factorization (PMF) receptor model were jointly employed to evaluate PTEs pollution levels and quantitatively apportion its sources. The results showed that PTEs concentrations in the mainstream were significantly higher than those in the tributaries, with Fe and Mn being the primary contaminants exceeding standards. During the DS, the mean concentrations of Fe and Mn were 1.33 mg/L and 0.34 mg/L, with exceedance rates of 100% and 84.2%, respectively. In contrast, both concentrations declined markedly in the FS (Fe: 0.27 mg/L; Mn: 0.112 mg/L). The PMF model identified three sources in the DS, with contribution rates of 42.1% (geogenic background and domestic sewage), 32.4% (industrial wastewater), and 25.5% (agricultural sources). In the FS, two sources were resolved, namely a mixture of non-point source pollution and domestic sewage (64.3%) and a mixture of geogenic background and industrial wastewater (35.7%). The pronounced increase in non-point source contribution during the FS highlights the role of rainfall runoff in driving pollutant input. This study provides a scientific basis for PTEs pollution control in the LYR. Full article

Indian metropolitan cities such as Mumbai grapple with rapid urbanisation, extreme urban density, high built-up areas, loss of green cover, and shrinking open spaces, resulting in increased impermeable surfaces, urban heat island effects, and frequent flooding occurrences. Modern stormwater management has increasingly been characterised by integrated grey-green approaches; however, cities in the Global North benefit from established policies, technical expertise, and financial resources that enable the systematic and large-scale integration of Blue-Green Infrastructure (BGI) through district-wide geospatial assessment frameworks, unlike many cities in the Global South. Despite growing interest in nature-based stormwater solutions, there remains a dearth of geospatial empirical research from India examining the placement, distribution, performance, and functionality of BGI integrated with existing stormwater management systems in cities such as Mumbai. Furthermore, hydrological modelling using tools such as the Storm Water Management Model (SWMM) for the design, planning, and implementation of BGI in Indian cities remains largely unexplored. This study explores the role of BGI strategies in improving urban stormwater management within high-density Indian cities under a 25-year return period extreme rainfall scenario. Using an integrated approach that combines QGIS-based spatial analysis with EPA-SWMM hydrologic-hydraulic modelling, the research examines runoff behaviour, identifies flooding hotspots, and evaluates the effectiveness of Low Impact Development (LID)-based BGI measures such as permeable pavements, infiltration trenches, and green roofs applied at the ward level in Mumbai’s F/North and G/North Wards. Detailed land use classification, spatial mapping, and rainfall simulation corresponding specifically to a 25-year return period rainfall event was used to assess pre- and post-intervention conditions. The findings indicate that the applied BGI measures led to a 12.6% reduction in peak runoff (137.6 m³/s to 120.2 m³/s) and a 5.5% decrease in total runoff volume (783,510 m³ to 740,410 m³). More importantly, the peak flooding flow rate decreased by 45% (94.1 m³/s to 51.7 m³/s), demonstrating that BGI measures can efficiently reduce peak flooding flows by extending runoff hydrographs during extreme rainfall events. These findings are specifically applicable to the simulated 25-year return period extreme rainfall scenario and may vary under different rainfall intensities or return periods. Less extreme events could potentially experience even greater relative reductions or prevent flooding altogether, while also easing downstream hydraulic loads. Overall, strategically placed BGI interventions can significantly reduce surface runoff and peak flow, thereby enhancing stormwater resilience within spatially constrained urban environments. This study provides a replicable, data-driven framework for catchment-scale stormwater planning in dense Indian cities under extreme rainfall conditions, offering practical insights into methods, local contextual considerations, and spatial planning strategies for policymakers and urban planners seeking to retrofit and adapt existing infrastructure under increasing hydrologic stress and climate variability. Full article

Accurate streamflow prediction in small catchments remains challenging due to their rapid response times, threshold-driven behaviors, and high spatial heterogeneity. This study develops and evaluates a novel modeling approach combining physics-informed feature selection with machine learning algorithms. Overall, 1825 model configurations were tested across fifteen algorithms (including Random Forest, XGBoost, LightGBM, CatBoost, Support Vector Machines, and deep learning methods) using multiple physics-informed input structures based on classical rainfall–runoff theory and mass balance conservation. Models were evaluated for predicting minimum, average, and maximum daily water levels and discharge. Results demonstrate that models structured around Green-Ampt infiltration assumptions consistently outperformed alternative configurations, with Random Forest achieving good performance for water level predictions. Causal models outperformed autoregressive approaches while the residuals analysis showed limitations in predicting extreme values. Feature importance analysis revealed that channel and catchment morphology and initial soil moisture conditions were dominant predictors, aligning with hydrological process understanding. Full article

The Tomarrazón-Camarones Basin (La Guajira, Colombia) is characterized by frequent, widespread flooding and, anthropogenically, by intense instream sediment mining. Mapping flood hazard is hence essential to develop effective flood management plans, and a knowledge of the water regime (duration curves) is also essential to estimate sediment transport and carry out sediment budgets to inform on the impacts and sustainability of the mining activity. However, neither water levels nor discharges are monitored by official gauging stations, and only a few rainfall gauging stations are available in the area, with daily records often affected by data gaps. Therefore, a first challenge is to reconstruct discharge time series by an affordable effort, scaled to the financial-labour resources available in that challenging context. This paper presents an integrated approach that combines satellite-derived rainfall data with ground observations. A semi-distributed hydrological model (HEC-HMS, SCS-CN method) is used to reconstruct the full flow-rate time series once calibrated and validated with data derived from automatic sensors and field measurements. The model is fed with hourly data derived from daily data at ground gauging stations temporally downscaled by adopting the spatially distributed hourly rainfall patterns obtained from satellite records. Before that, observed water levels in three stations equipped with water level sensors were translated into discharge time series using analytical relationships based on field-measured geometric and physical characteristics. Then, these event-based hydrographs were used to calibrate and validate the model. Results show good agreement with observations, with R² = 0.981 and a relative RMSE of 40% for overall hydrograph reproduction, and R² = 0.87 for peak flow estimation, supporting a reasonable confidence in the approach. The calibrated model is then applied to long-term datasets (1973–2024) to retrieve duration curves and return periods of peak discharges. Full article

This study established a refined, distributed SWAT modeling framework that integrates elevation-band and snowmelt modules to reconstruct the alpine hydrological and sediment cycles of the Koshi River Basin (KRB) over the period 1990–2024, with climate scenarios constructed using the delta change approach. The KRB, a major transboundary watershed traversing China, Nepal, and India, was selected owing to its critical hydro-climatic role under the destabilizing “Asian Water Tower”; it generates substantial sediment yield, hosts the densest concentration of hydropower potential within the Ganges system, and spans an extreme vertical gradient from Mount Everest to the southern alluvial plains. Results reveal accelerated warming at a rate of 0.21 °C per decade and an overall warming–wetting trend, punctuated by an abrupt interdecadal shift around 2015. Precipitation dominated interannual streamflow variability, with enhanced rainfall triggering basin-wide sediment surges that overwhelmed the natural buffering capacity of the land surface. Conversely, rising temperatures intensified actual evapotranspiration, markedly depleting soil water and reducing total water yield and monsoon runoff, although sustained snow and glacier melt effectively elevated the dry-season low-flow baseline. The integrated climate forcing reshaped the disparity between hydrological extremes, imposing severe seasonal eco-hydrological stress that manifested as a pre-monsoon deficit in terrestrial green water and acute summer sediment outbursts for aquatic habitats. Furthermore, the flood regime exhibited an altered distribution, with mid-to-high frequency floods enhanced while low-frequency extreme flood peaks declined. The hydro-sedimentological regime consequently exhibits pronounced nonlinear responses to climate change, providing a critical, threshold-based scientific foundation for adaptive transboundary water resource management. Full article