Search Results (1,235)

Focusing on the Lingshan section of Guangxi’s Pinglu Canal, this study addresses frequent waterlogging during construction under subtropical monsoon rainfall. Human disturbances alter hydrological processes, causing project delays and economic losses. We developed a coupled Storm Water Management Model (SWMM 1D hydrological) and Hydrologic Engineering Center—River Analysis System 2D (HEC-RAS 2D hydrodynamic) model. High-resolution Unmanned Aerial Vehicle—Light Detection and Ranging (UAV-LiDAR) Digital Elevation Model (DEM) delineated sub-catchments, while the Green-Ampt model quantified soil conductivity decay. Synchronized runoff data drove high-resolution HEC-RAS 2D simulations of waterlogging evolution under design storms (1–100-year return periods) and a real event (10 May 2025). Key results: Water depth exhibits nonlinear growth with return period—slow at low intensities but accelerating beyond 50-year events, particularly at temporary road junctions where embankments impede flow. Additionally, intensive intermittent rainfall causes significant ponding at excavation pit-road intersections, and optimized drainage drastically shortens recession time. The study reveals a “rapid runoff generation–restricted convergence–prolonged ponding” mechanism under construction disturbance, validates the model’s capability for complex scenarios, and provides critical data for real-time waterlogging risk prediction and drainage optimization during the canal’s construction. Full article

The rapid expansion of urbanization and inadequate planning have triggered a water balance crisis in many cities, manifesting as both the need for artificial lake supplementation and frequent urban flooding. Using the Xuanwu Lake watershed in Nanjing as a case study, this research aims to optimize the Blue–Green Infrastructure (BGI) network to maximize rainfall utilization within the watershed. The ultimate goal is to restore natural water balance processes and reduce reliance on artificial supplementation while mitigating urban flood risks. First, the Soil Conservation Service Curve Number (SCS–CN) model is employed to estimate the maximum potential of natural convergent flow within the watershed. Second, drawing on landscape connectivity theory, a multi-level BGI network optimization model is developed by integrating the Minimum Cumulative Resistance (MCR) model and the gravity model, incorporating both hydrological connectivity and flood safety considerations. Third, a water balance model based on the Storm Water Management Model (SWMM) framework and empirical formulas is constructed and coupled with the network optimization model to simulate and evaluate water budget performance under optimized scenarios. The results indicate that the optimized scheme can reduce artificial supplementation to Xuanwu Lake by 62.2% in June, while also ensuring effective supplementation throughout the year. Annual runoff entering the lake reaches 13.25 million cubic meters, meeting approximately 13% of the current annual supplementation demand. Moreover, under a 100-year return period flood scenario, the optimized network reduces total watershed flood volume by 35% compared to pre-optimization conditions, with flood-prone units experiencing reductions exceeding 50%. These findings underscore the optimized BGI network scheme’s capacity to reallocate rainwater resources efficiently, promoting a transition in urban water governance from an “engineering-dominated” approach to an “ecology-oriented and self-regulating” paradigm. Full article

Hydrological time series frequently exhibit periodic trends with variables such as rainfall, runoff, and evaporation rates often following annual cycles. Seasonal variations further contribute to the complexity of these data sets. A critical aspect of analyzing such phenomena is estimating realistic return intervals, making the precise determination of these values essential. Given this importance, selecting an appropriate probability distribution is paramount. To address this need, we introduce a flexible probability model specifically designed to capture periodicity in hydrological data. We thoroughly examine its fundamental mathematical and statistical properties, including the asymptotic behavior of the probability density function (PDF) and hazard rate function (HRF), to enhance predictive accuracy. Our analysis reveals that the PDF exhibits polynomial decay as $x\to \infty$, ensuring heavy-tailed behavior suitable for extreme events. The HRF demonstrates decreasing or non-monotonic trends, reflecting variable failure risks over time. Additionally, we conduct a simulation study to evaluate the performance of the estimation method. Based on these results, we refine return period estimates, providing more reliable and robust hydrological assessments. This approach ensures that the model not only fits observed data but also captures the underlying dynamics of hydrological extremes. Full article

Urban and peri-urban lakes are increasingly threatened by water quality degradation due to rising anthropogenic pressures and environmental variability. This study proposes an integrated framework that combines multi-source data and machine learning to estimate and monitor three key water quality parameters: turbidity, total dissolved solids (TDS), and biological oxygen demand (BOD). Field measurements from three lakes in West Bengal, India, Rabindra Sarovar, Mirikh Lake, and Hanuman Ghat Lake, were combined with Landsat-8 satellite imagery, meteorological data, and land use information. Three modeling scenarios were developed: (i) using only remote sensing indices, (ii) combining remote sensing indices with meteorological variables, and (iii) integrating remote sensing indices, meteorological data, and land use features. Principal component analysis (PCA) was used to reduce dimensionality and redundancy. Machine learning models, namely, XGBoost, Decision Tree, and Ridge Regression, were trained and evaluated using R² and RMSE (Root Mean Square Error) metrics. The third scenario outperformed the others, with Ridge Regression achieving the highest accuracy for BOD prediction (R² = 0.99). Spatiotemporal patterns revealed persistently high BOD levels along urban lake fringes and post-monsoon spikes in turbidity and TDS, especially in agriculturally influenced zones. These patterns were closely linked to land use practices, rainfall-driven runoff, and point-source pollution. This study underscores the effectiveness of remote sensing and machine learning as scalable tools for real-time water quality monitoring, promoting sustainability through informed lake management strategies in India. Full article

Flash floods, exacerbated by climate change and land use alterations, are among the most destructive natural hazards globally, leading to significant damage and loss of life. In this context, the Flash Flood Potential Index (FFPI), which is a terrain and land surface-based model, and Google Earth Engine (GEE) were used to assess flood-prone zones across North Macedonia’s watersheds. The presented GEE-based assessment was accomplished by a custom script that automates the FFPI calculation process by integrating key factors derived from publicly available sources. These factors, which define susceptibility to torrential floods, include slope (Copernicus GLO-30 DEM), land cover (Copernicus GLO-30 DEM), soil type (SoilGrids), vegetation (ESA World Cover), and erodibility (CHIRPS). The spatial distribution of average FFPI values across 1396 small catchments (10–100 km²) revealed that a total of 45.4% of the area exhibited high to very high susceptibility, with notable spatial variability. The CHIRPS rainfall data (2000–2024) that combines satellite imagery and in situ measurements was used to estimate peak 24 h runoff and discharge. To improve the accuracy of CHIRPS, the data were adjusted by 30–50% to align with meteorological station records, along with normalized FFPI values as runoff coefficients. Validation against 328 historical river flood and flash flood records confirmed that 73.2% of events aligned with moderate to very high flash flood susceptibility catchments, underscoring the model’s reliability. Thus, the presented cloud-based scenario highlights the potential of the GEE’s efficacy in scalability and robustness for flash flood modeling and regional risk management at national scale. Full article

Straw fiber curtain contains a plant fiber blanket woven from crop straw, which is mainly used to protect embankment slopes from rainwater erosion. To investigate the erosion control performance of slopes covered with straw fiber curtains of different structural configurations, physical model tests were conducted in a 95 cm × 65 cm × 50 cm (length × height × width) test box with a slope ratio of 1:1.5 under controlled artificial rainfall conditions (20 mm/h, 40 mm/h, and 60 mm/h). The study evaluated the runoff characteristics, sediment yield, and key hydrodynamic parameters of slopes under the coverage of different straw fiber curtain types. The results show that the A-type straw fiber curtain (woven with strips of straw fiber) has the best effect on water retention and sediment reduction, while the B-type straw fiber curtain (woven with thicker straw strips) with vertical straw fiber has a better effect regarding water retention and sediment reduction than the B-type transverse straw fiber curtain. The flow of rainwater on a slope covered with straw fiber curtain is mainly a laminar flow. Straw fiber curtain can promote the conversion of water flow from rapids to slow flow. The Darcy-Weisbach resistance coefficient of straw fiber curtain increases at different degrees with an increase in rainfall time. Full article

This study focuses on developing a climate-flood model to investigate and interpret the relationship and impact of climate on runoff/flooding at a sub-regional scale using multiple linear regression (MLR) with 30 years of hydro-climatic data for the Cross River Basin, Nigeria. Data were obtained from Nigerian Meteorological Agency (NIMET) for the following climatic parameters: annual average rainfall, maximum and minimum temperatures, humidity, duration of sunlight (sunshine hours), evaporation, wind speed, soil temperature, cloud cover, solar radiation, and atmospheric pressure. These hydro-meteorological data were analysed and used as parameters input to the climate-flood model. Results from multiple regression analyses were used to develop climate-flood models for all the gauge stations in the basin. The findings suggest that at 95% confidence, the climate-flood model was effective in forecasting the annual runoff at all the stations. The findings also identified the climatic parameters that were responsible for 100% of the runoff variability in Calabar (R² = 1.000), 100% the runoff in Uyo (R² = 1.000), 98.8% of the runoff in Ogoja (R² = 0.988), and 99.9% of the runoff in Eket (R² = 0.999). Based on the model, rainfall depth is the only climate parameter that significantly predicts runoff at 95% confidence intervals in Calabar, while in Ogoja, rainfall depth, temperature, and evaporation significantly predict runoff. In Eket, rainfall depth, relative humidity, solar radiation, and soil temperatures are significant predictors of runoff. The model also reveals that rainfall depth and evaporation are significant predictors of runoff in Uyo. The outcome of the study suggests that climate change has impacted runoff and flooding within the Cross River Basin. Full article

African hydrological systems are incredibly complex and highly sensitive to climate variability. This review synthesizes observational data, remote sensing, and climate modeling to understand the interactions between fluvial processes, water cycle dynamics, and anthropogenic pressures. Currently, these systems are experiencing accelerating warming (+0.3 °C/decade), leading to more intense hydrological extremes and regionally varied responses. For example, East Africa has shown reversed temperature–moisture correlations since the Holocene onset, while West African rivers demonstrate nonlinear runoff sensitivity (a threefold reduction per unit decline in rainfall). Land-use and land-cover changes (LULCC) are as impactful as climate change, with analysis from 1959–2014 revealing extensive conversion of primary non-forest land and a more than sixfold increase in the intensity of pastureland expansion by the early 21st century. Future projections, exemplified by studies in basins like Ethiopia’s Gilgel Gibe and Ghana’s Vea, indicate escalating aridity with significant reductions in surface runoff and groundwater recharge, increasing aquifer stress. These findings underscore the need for integrated adaptation strategies that leverage remote sensing, nature-based solutions, and transboundary governance to build resilient water futures across Africa’s diverse basins. Full article

Climate change is projected to significantly alter hydrological conditions across the Northern Hemisphere, with increased precipitation variability, more intense rainfall events, and earlier, rain-driven spring floods in regions like northern Sweden. These changes will affect both natural ecosystems and hydropower-regulated rivers, particularly during ecologically sensitive periods such as the grayling spawning season in late spring. This study examines the impact of extreme spring flow conditions on grayling spawning habitats by analyzing historical runoff data and simulating high-flow events using a 2D hydraulic model in Delft3D FM. Results show that previously suitable spawning areas became too deep or experienced flow velocities beyond ecological thresholds, rendering them unsuitable. These hydrodynamic shifts could have cascading effects on aquatic vegetation and food availability, ultimately threatening the survival and reproductive success of grayling populations. The findings underscore the importance of integrating ecological considerations into future water management and hydropower operation strategies in the face of climate-driven flow variability. Full article

Water migration occurs in unsaturated loess subgrade due to extreme rainfall, making it prone to subgrade subsidence and other water damage disasters, which seriously impact road safety and sustainable development of the Loess Plateau. The study performed a rainfall test using a compacted loess subgrade model based on a self-developed water migration test device. The effects of extreme rainfall on the water distribution, wetting front, and infiltration rate in the subgrade were systematically explored by setting three rainfall intensities (4.6478 mm/h, 9.2951 mm/h, and 13.9427 mm/h, namely J1 stage, J2stage, and J3 stage), and a lateral water migration model was proposed. The results indicated that the range of water content change areas constantly expands as rainfall intensity and time increase. The soil infiltration rate gradually decreased, and the ratio of surface runoff to infiltration rainfall increased. The hysteresis of lateral water migration refers to the physical phenomenon in which the internal water response of the subgrade is delayed in time and space compared to changes in boundary conditions. The sensor closest to the side of the slope changed first, with the most significant fluctuations. The farther away from the slope, the slower the response and the smaller the fluctuation. The bigger the rainfall intensity, the faster the wetting front moved horizontally. The migration rate at the slope toe is the highest. The migration rate of sensor W3 increased by 66.47% and 333.70%, respectively, in the J3 stage compared to the J2 and J1 stages. The results of the model and the measured data were in good agreement, with the R² exceeding 0.90, which verifies the reliability of the model. The study findings are important for guiding the prevention and control of disasters caused by water damage to roadbeds in loess areas. Full article

The hydrological response of a watershed is strongly influenced by the spatiotemporal dynamics of rainfall. Rainfall events of similar magnitude can produce markedly different flood processes due to variations in the spatiotemporal patterns of rainfall, posing significant challenges for flood forecasting under complex rainfall scenarios. Traditional methods typically rely on high-resolution or synthetic rainfall data to characterize the scale, direction and velocity of rainstorms, in order to analyze their impact on the flood process. These studies have shown that storms traveling along the main river channel tend to exert the greatest impact on flood processes. Therefore, tracking the movement of the rainfall center along the flow direction, especially when only rain gauge data are available, can reduce model complexity while maintaining forecast accuracy and improving model applicability. This study proposes a machine learning-based time-variable instantaneous unit hydrograph that integrates rainfall spatiotemporal dynamics using quantitative spatial indicators. To overcome limitations of traditional variable unit hydrograph methods, a pre-training and fine-tuning strategy is employed to link the unit hydrograph S-curve with rainfall spatial distribution. First, synthetic pre-training data were used to enable the machine learning model to learn the shape of the S-curve and its general pattern of variation with rainfall spatial distribution. Then, real flood data were employed to learn the actual runoff routing characteristics of the study area. The improved model allows the unit hydrograph to adapt dynamically to rainfall evolution during the flood event, effectively capturing hydrological responses under varying spatiotemporal patterns. The case study shows that the improved model exhibits superior performance across all runoff routing metrics under spatiotemporal rainfall variability. The improved model increased the simulation qualified rate for historical flood events, with significant rainfall center movement during the event from 63% to 90%. This study deepens the understanding of how rainfall dynamics influence watershed response and enhances hourly-scale flood forecasting, providing support for disaster early warning with strong theoretical and practical significance. Full article

Non-Newtonian debris flows represent a critical challenge for hydraulic infrastructure in mountainous regions, often causing significant damage and service disruption. However, current models typically simplify these flows as Newtonian, leading to inaccurate design assumptions. This study addresses this gap by comparing the hydraulic behavior of Newtonian and non-Newtonian flows in an alluvial fan, using the Amoray Gully in Apurímac, Peru, as a case study. This gully intersects the Interoceánica Sur national highway via a low-water crossing (baden), making it a relevant site for evaluating debris flow impacts on critical road infrastructure. The methodology integrates hydrological analysis, rheological characterization, and hydraulic modeling. QGIS 3.16 was used for watershed delineation and extraction of physiographic parameters, while a high-resolution topographic survey was conducted using an RTK drone. Rainfall-runoff modeling was performed in HEC-HMS 4.7 using 25 years of precipitation data, and hydraulic simulations were executed in HEC-RAS 6.6, incorporating rheological parameters and calibrated with the footprint of a historical event (5-year return period). Results show that traditional Newtonian models underestimate flow depth by 17% and overestimate velocity by 54%, primarily due to unaccounted particle-collision effects. Based on these findings, a multi-barrel circular culvert was designed to improve debris flow management. This study provides a replicable modeling framework for debris-prone watersheds and contributes to improving design standards in complex terrain. The proposed methodology and findings offer practical guidance for hydraulic design in mountainous terrain affected by debris flows, especially where infrastructure intersects active alluvial fans. Full article

Flood hazards have increasingly threatened the East Kazakhstan region in recent decades due to climate change and growing anthropogenic pressures, leading to more frequent and severe flooding events. This article considers an approach to modeling and forecasting river runoff using the example of the small Kurchum River in the East Kazakhstan region. The main objective of this study was to evaluate the numerical performance of the flood hazard model by comparing simulated flood extents with observed flood data. Two types of data were used as initial data: topographic data (digital elevation models and topographic maps) and hydrological data, including streamflow time series from stream gauges (hourly time steps) and lateral inflows along the river course. Spatially distributed rainfall forcing was not applied. To build the model, we used the software packages of HEC-RAS version 5.0.5 and MIKE version 11. Using retrospective data for 3 years (2019–2021), modeling was performed, the calculated boundaries of possible flooding were obtained, and the highest risk zones were identified. A dynamic map of depth changes in the river system is presented, showing the process of flood wave propagation, the dynamics of depth changes, and the expansion of the flood zone. Temporal flood inundation mapping and performance metrics were evaluated for each individual flood event (2019, 2020, and 2021). The simulation outcomes closely correlate with actual flood events. The assessment showed that the model data coincide with the real ones by 91.89% (2019), 89.09% (2020), and 95.91% (2021). The obtained results allow for a clarification of potential flood zones and can be used in planning measures to reduce flood risks. This study demonstrates the importance of an integrated approach to modeling, combining various software packages and data sources. Full article

The hydrological processes of agricultural fields during the fallow season in east-central Mississippi remain poorly understood, due to the region’s unique rainfall patterns. This study utilized long-term rainfall records from 1924 to 2023 to evaluate runoff characteristics and the runoff response to various rainfall events during fallow seasons in Mississippi by applying the DRAINMOD model. The analysis revealed that the average rainfall during the fallow season was 760 mm over the past 100 years, accounting for 65% of the annual total. In dry, normal, and wet fallow seasons, the average rainfall was 528, 751, and 1010 mm, respectively, corresponding to runoff of 227, 388, and 602 mm. Runoff frequency increased with wetter weather conditions, rising from 16 events in dry seasons to 23 in normal seasons and 30 in wet seasons. Over the past century, runoff dynamics were predominantly regulated by high-intensity rainfall events during the fallow season. Very heavy rainfall events (mean frequency = 11 events) generated 215 mm of runoff and accounted for 53% of the total runoff, while extreme rainfall events (mean frequency = 2 events) contributed 135 mm of runoff, making up 34% of the total runoff. Water table depth played a critical role in shaping spring runoff dynamics. As the water table decreased from 46 mm in March to 80 mm in May, the soil pore space increased from 5 mm in March to 14 mm in May. This increased soil infiltration and water storage capacity, leading to a steady decline in runoff. The study found that the mean daily runoff frequency dropped from 13.5% in March to 7.6% in May, while monthly runoff decreased from 74 to 38 mm. Increased extreme rainfall (R95p) in April contributed over 45% of the total runoff and resulted in the highest daily mean runoff of 20 mm, compared to 18 mm in March and 16 mm in May. The results from this century-long historical weather data could be used to enhance field-scale water resource management, predict potential runoff risks, and optimize planting windows in the humid east-central Mississippi. Full article

The U.S. Environmental Protection Agency (EPA) developed the concept of Water Quality Volume (WQv) as a Best Management Practice (BMP) to treat the first 25.4 mm of rainfall in urban areas, aiming to capture approximately 90% of annual runoff. However, applying this urban-based standard—designed for areas with over 50% imperviousness—to rural regions with higher infiltration and pervious surfaces may result in overestimated facility capacities. In Korea, a uniform WQv criterion of 5 mm is applied nationwide, regardless of land use or hydrological conditions. This study examines the suitability of this 5 mm standard in rural catchments using the Hydrological Simulation Program–Fortran (HSPF). Eight sub-watersheds in the target area were simulated under varying cumulative runoff depths (1–10 mm) to assess pollutant loads and runoff characteristics. First-flush effects were most evident below 5 mm, with variation depending on land cover. Nature-based treatment systems for constructed wetlands were modeled for each sub-watershed, and their effectiveness was evaluated using Flow Duration Curves (FDCs) and Load Duration Curves (LDCs). The findings suggest that the uniform 5 mm WQv criterion may result in overdesign in rural watersheds and highlight the need for region-specific standards that consider local land-use and hydrological variability. Full article