Search Results (285)

This study investigates how landcover change between 1935 and 2020 have shaped hydrological responses in the semi-arid highlands of Tigray, Ethiopia. Focusing on the Tsili catchment (27.5 km²), it examines links between landcover change, drainage network evolution, and river channel width under conditions of population growth and climate variability. Landcover and drainage maps were derived from historical aerial photographs and satellite imagery for four time steps, and surface runoff was simulated using the SWAT model with uniform meteorological forcing to isolate landcover effects. Results show a 37.6% increase in cropland and substantial declines in shrubland (−29.3%) and forest (−10.1%). River channel width at the outlet widened from 7.5 to 10.5 m, while drainage density increased 1.5-fold. These physical changes aligned with modelled increases in surface runoff. Strong correlations were found between runoff, channel width, drainage density, and landcover types. The findings highlight that cropland expansion—at the expense of natural vegetated land—has intensified runoff and erosion risks. As climate change is expected to bring more intense rainfall to East Africa, this underscores the need for land management strategies that reduce hydrological connectivity and support sustainable agriculture in data-scarce regions. Full article

Urban pluvial waterlogging has become a major challenge for densely populated cities due to increasingly extreme rainfall events and the rapid expansion of impervious surfaces. In response to the growing demand for localized waterlogging risk assessments, an automated evaluation framework is proposed that integrates high-resolution digital elevation models (DEMs), rainfall scenarios, and classified building data within a GIS-based modeling system. The methodology consists of four modules: (i) design of rainfall scenarios and runoff estimation, (ii) waterlogging depth simulation based on volume-matching algorithms, (iii) construction of depth–damage curves for residential and commercial buildings, and (iv) building-level economic loss estimation though differentiated depth–damage functions for residential/commercial assets—a core innovation enabling sector-specific risk precision. A case study was conducted in the Lixia District, Jinan City, China, involving 15,317 buildings under a 50-year return period rainfall event. The total economic losses were shown to reach approximately USD 327.88 million, with residential buildings accounting for 88.6% of the total. The model achieved a mean absolute percentage error within 5% for both residential and commercial cases. The proposed framework supports high-precision, building-level urban waterlogging damage assessment and demonstrates scalability for use in other high-density urban areas. Note: all monetary values were converted from Chinese Yuan (CNY) to U.S. Dollars (USD) using an average exchange rate of 1 USD = 7.28 CNY. Full article

Recent flooding events in Spain have highlighted the need to develop real-time flood forecasts to estimate streamflows over the next few hours and days. Therefore, a meteorological forecast that provides possible precipitation for the upcoming hours combined with a hydrological model to simulate the rainfall-runoff processes in the basin and its flood response are needed. In this paper, a probabilistic flood forecasting tool is proposed for the Francolí river basin, located in Catalonia (Spain). For this purpose, the Real-time Interactive Basin Simulator (RIBS) distributed hydrological model was calibrated in this basin for a set of flood events. Then, a series of rainfall field forecasts based on the analog method have been used as input data in the hydrological model, obtaining a set of hydrographs for given flood events as output. Finally, a probabilistic forecast that supplies the probability distribution of the possible response flows of the Francolí river is provided for a set of episodes. Full article

The presence of understory vegetation not only influences slope-scale soil and water conservation but also exerts a profound effect on hydrodynamic characteristics and the processes of runoff and sediment production. Therefore, in this study, different vegetation types and vegetation coverages (bare land, 30%, 60%, and 90%) were set up by simulating rainfall (45, 60, 90, and 120 mm·h⁻¹) to evaluate the runoff-sediment process and the response characteristics of hydrodynamic parameters. The results showed that increasing vegetation cover significantly reduced soil erosion on forest slopes (p < 0.05). When the vegetation cover ranged from 60% to 90%, vegetation pattern C and pattern D were the most effective in suppressing erosion, where increased cover improved runoff stability. Under low-cover conditions, overland flow tended toward turbulent and rapid regimes, whereas under high cover conditions, flow was primarily laminar and slow. Patterns C and D significantly reduced flow velocity and water depth (p < 0.05). Structural equation patterning revealed that, under different vegetation patterns, the runoff power (ω), Reynolds number (Re), and resistance coefficient (f) more effectively characterized the erosion process. Among these, the Reynolds number and runoff power were the dominant factors driving erosion on red soil slopes. By contrast, runoff shear stress was significantly reduced under high-cover conditions and showed weak correlation with sediment yield, suggesting that it was unsuitable as an indicator of slope erosion. Segmental vegetation arrangements and increasing vegetation cover near runoff outlets—especially at 60–90% coverage—effectively reduced soil erosion. These findings provide scientific insight into the hydrodynamic mechanisms of vegetation cover on slopes and offer theoretical support for optimizing soil and water conservation strategies on hilly terrain. 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

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

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

Urbanization and climate change have disrupted natural water circulation by increasing impervious surfaces and altering rainfall patterns, leading to reduced groundwater infiltration, deteriorating water quality, and heightened flood risks. This study investigates the application of Low Impact Development (LID) and flood control facilities as structural measures to address these challenges in the upper watershed of the Fucha River in Bogotá, Colombia. The methodology involved analyzing watershed characteristics, defining circulation problems, setting hydrological targets, selecting facility types and locations, evaluating performance, and conducting an economic analysis. To manage the target rainfall of $26.5$$\mathrm{m}$$\mathrm{m}$ under normal conditions, LID facilities such as vegetated swales, rain gardens, infiltration channels, and porous pavements were applied, managing approximately 2362 ${\mathrm{m}}^3$ of runoff. For flood control, five detention tanks were proposed, resulting in a 31.8% reduction in peak flow and a 7.3% decrease in total runoff volume. The flooded area downstream was reduced by $46.8$$\mathrm{h}\mathrm{a}$, and the benefit–cost ratio was calculated at 1.02. These findings confirm that strategic application of LID and detention facilities can contribute to effective urban water cycle management and disaster risk reduction. While the current disaster management approach in Bogotá primarily focuses on post-event response, this study highlights the necessity of transitioning toward proactive disaster preparedness. In particular, the introduction and expansion of flood forecasting and warning systems are recommended as non-structural measures, especially in urban areas with complex infrastructure and climate-sensitive hydrology. Full article

The effects of climate change and urbanisation, such as more intense rainfall and changing land use patterns, are putting increasing pressure on urban drainage systems. This study proposes a comprehensive methodology for evaluating the effectiveness of sustainable urban drainage systems (SUDSs) in mitigating flooding and managing stormwater in both current and future scenarios. The approach integrates geospatial data, including digital elevation models (DEMs) and land use information, to delineate catchments and characterise hydrological parameters. Historical rainfall records and hydrological modelling were employed to define two baseline storm events: an extreme storm involving 422 mm of rainfall over 2 h, and an average storm involving 2.84 mm of rainfall over 1 h and 18 min. Future scenarios were developed by updating these baseline events using annual rates of change in maximum and average precipitation derived from climate projections between 2025 and 2100. The analysis incorporates seven CMIP6 climate scenarios: SSP1-1.9, SSP1-2.6, SSP4-3.4, SSP4-2.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5. A stochastic simulation of 1000 storms per year was carried out using a custom-built conceptual hydrological model based on CN and developed in Python, which reflects interannual variability. The results show that extreme storm volumes could increase by up to seven times and average storm volumes by up to two and a half times. Additionally, discharge peaks could exceed baseline values by up to 20% in some years, suggesting an increased occurrence of extreme runoff events. The methodology assesses SUDS performance by comparing runoff and hydrological responses between baseline and future estimates. This framework enables vulnerabilities and adaptation needs to be identified, ensuring the long-term effectiveness of SUDSs in managing urban flood risk. Addressing uncertainties in climate and land use projections emphasises the importance of integrating SUDS assessments into wider urban resilience strategies. Full article

The reducing streamflow is a major concern in the Yilong Lake Basin (YLB), which supplies water for agriculture and the growing population in the basin and to maintain the health of the regional ecosystem. The YLB has experienced remarkable land use/land cover change (LUCC) and climate change (CC) in recent years. To understand the drivers of the streamflow change in this basin, the effects of the land use change and climate variation on the temporal flow variability were studied using the Soil and Water Assessment Tool (SWAT). The calibration and validation results indicated that the SWAT simulated the streamflow well. Then the streamflow responses to the land use change between 2010 and 2020 and climate change with future climate projections (SSP245, SSP370, and SSP585) were evaluated. Results showed that the LUCC in the YLB caused a marginal decline in the annual streamflow at the whole basin scale but significantly altered rainfall–runoff relationships and intra-annual discharge patterns; e.g., monthly streamflows decreased by up to 3% in the dry season under the surface modification, with subbasins of the YLB exhibiting divergent responses attributed to spatial heterogeneity in land surface transitions. Under future climate scenarios, streamflow projections revealed general declining trends with significant uncertainties, particularly under high-emission pathways, e.g., SSP370 and SSP585, in which the streamflow could be projected to reduce by up to 5.9% in the mid-future (2031–2045). In addition, droughts were expected to intensify, exacerbating seasonal water stress in the future. It suggests that integrated water governance should synergize climate-resilient land use policies with adaptive infrastructure to address regional water resources challenges. Full article

Catastrophic floods in monsoon-driven river systems pose significant challenges to flood resilience. In July 2020, China’s Huai River Basin (HRB) encountered an unprecedented basin-wide flood event characterized by anomalous southward displacement of the rain belt. This event established a new historical record with the three typical hydrological stations (Wangjiaba, Runheji, and Lutaizi sections) along the mainstem of the Huai River exceeded their guaranteed water levels within 11 h and synchronously reached peak flood levels within a 9-h window, whereas the inter-station lag times during the 2003 and 2007 floods ranged from 24 to 48 h, causing a critical emergency in the flood defense. By integrating operational hydrological data, meteorological reports, and empirical rainfall-runoff model schemes for the Meiyu periods of 2003, 2007, and 2020, this research systematically dissects the 2020 flood’s spatial composition patterns. Comparative analyses across spatiotemporal rainfall distribution, intensity metrics, and flood peak response dynamics reveal distinct characteristics of southward-shifted torrential rain and flood variability. The findings provide critical technical guidance for defending against extreme weather events and unprecedented hydrological disasters, directly supporting revisions to flood control planning in the Huai River Ecological and Economic Zone. Full article

Flood events have become more frequent as a result of seasonal changes, global warming, and changes in sea level. In terms of basin management, it is necessary to know the hydrodynamics of the basin in order to produce faster solutions in emergency action plans. The Filyos River is one of the two most important floodplains in the western Black Sea basin and has so far only been analyzed to a limited extent using modern hydrological and hydraulic models. In order to analyze the flood dynamics and determine the flood risks in the Filyos River. In this context, flood hydrographs, rainfall depths, peak flows, and excess water volumes were calculated for different return periods (2, 5, 10, 20, 50, and 100 years) using HEC-RAS, HEC-HMS, and Hyfran Plus software. The analyses showed that the rainfall depth and peak flow rate increased significantly as the return period increased. It was also observed that although the volume of precipitation increased, the amount of water converted into surface runoff remained limited due to infiltration and other losses. The results of the study contribute to the identification of high flood-risk areas in the Filyos River basin, the improvement of flood prevention infrastructure, and the development of sustainable water management policies. Analyses using modeling tools such as HEC-RAS and HEC-HMS provide a scientific basis to help local governments and decision makers strengthen flood prevention strategies, update risk maps, and make emergency response plans more effective while making flood scenarios more reliable. Full article