Search Results (2,165)

Assessing environmental flows downstream of reservoirs under changing climate and increasing water demand remains a critical challenge in catchment management. This study presents an integrated framework for optimizing environmental flow releases by explicitly linking reservoir operation with climate change and population growth. The key novelty lies in the development of a modified objective function that incorporates environmental flow requirements alongside evolving hydrological and demand conditions. Reservoir inflows were simulated using an artificial intelligence-based rainfall–runoff model, employing a neuro-fuzzy inference system to capture nonlinear relationships between climate variables and runoff. Future rainfall projections were derived from four general circulation models (ACCESS1.0, CanESM2, MIROC5, and NorESM-M1) across four-time horizons (2021–2040, 2041–2060, 2061–2080, and 2081–2100). The simulated inflows were coupled with a reservoir operation model to optimize environmental flow releases, with system performance evaluated using reliability and vulnerability metrics. Results show that climate change alone has a limited impact on environmental flow supply; however, when combined with population-driven increases in water demand, significant reductions in system performance occur. In the worst-case scenario, the reliability of meeting environmental flow requirements drops below 20%, accompanied by a marked increase in system vulnerability. These findings demonstrate that water demand pressures play a dominant role in shaping future environmental flow availability. The proposed framework provides a robust and adaptable approach for integrating hydrological variability and socio-economic drivers into reservoir management, supporting more informed decision-making for balancing water supply and ecosystem sustainability under future uncertainty. Full article

Urban hydrological models require robust calibration strategies to accurately simulate rainfall–runoff processes, yet parameter sensitivity and the choice of objective functions remain key challenges. This study applied the SWMM nonlinear reservoir approach to an urban catchment in Pavia, Italy, using high-resolution rainfall–runoff data from multiple events. Sensitivity analysis showed that surface storage and hydraulic resistance dominated runoff response, while infiltration had limited influence. Calibration was performed with a genetic algorithm, testing six objective functions. The results indicated that the Index of Agreement and NSE-sqrt provided the most consistent performance across calibration and validation, supporting their use in urban drainage modeling. Full article

Gravity Recovery and Climate Experiment (GRACE) satellites primarily monitor changes in land water storage, including groundwater, soil moisture, lake and river surface water, and canopy and snow water. However, its coarse spatial resolution of 0.25 degrees limits its ability to observe smaller basins. To assess aquifer depletion and evaluate a long-term water resource management framework, GRACE data are crucial. It remains rare for GRACE-focused studies to be conducted in great depth. A comprehensive review of 80 articles published between 2011 and 2025 was conducted using the Scopus and Web of Science databases. These articles focused on downscaling GRACE data using machine learning (ML) methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were used in this review. This study highlights the attributes of ML models, the input variables used, the evaluation metrics, and the output resolution. Based on the analysis of the articles, random forest (RF) methods were used in the majority of the papers. Gradient boosting (GB), artificial neural networks (ANN), support vector machines (SVM), support vector regression (SVR), and long short-term memory (LSTM) were the most widely used ML methods. As input variables, rainfall (Pr), soil moisture (SM), and runoff (Qs) are essential. In 2011, there were very few journal articles; since 2021, the number has increased. The number of published studies from China was the highest (24), followed by the USA (12) and Iran (9). A total of 38 journals published reviewed articles. In terms of articles, Remote Sensing generates 19%, Journal of Hydrology has 10%, and Journal of Hydrology: Regional Studies has 8%. The paper also discusses limitations, challenges, recommendations, and potential future directions for improving the accuracy of the GWS change prediction model. Full article

Efficient storm drainage systems (SDSs) play a pivotal role in sustainable urban development. In arid regions, urban SDS often underperform during prolonged dry periods, leaving them inoperable due to sediment buildup and clogging from the intrusion of sprawling waste. Municipalities either rely on emergency response to flooding complaints or inspect storm sewers individually to handle flash floods and conserve high-value rainwater. The present study developed a risk-based decision-analysis framework for resource-efficient inspection and maintenance (I&M) planning of SDS to prioritize geographically clustered sub-zones. The study applied the framework to a case study of three urban zones with varying population densities and land use distributions in Buraydah, Qassim, Saudi Arabia. The framework integrates fuzzy synthetic evaluation (FSE) to address data limitations and subjective expert knowledge, with geographic information system (GIS)-based spatial analysis to assess three risk factors: likelihood, consequences, and detectability of sewer clogging potential. In addition to traditional likelihood-based evaluation of the susceptibility of smaller sewers to sediment accumulation due to performance anomalies, the consequence analysis augmented the process by considering land-use characteristics, exemplified by commercial areas exhibiting higher socio-economic losses than open spaces that buffer excess runoff. The detectability factor consolidated the decision analysis by incorporating the impacts of past delayed inspections, deep manholes, and scattered construction-related waste on clogging potential. The analysis identified sub-zones with aged sewers, deep manholes, long-awaited inspections, and high population densities, resulting in a high risk. GIS maps showing distinct impacts of the three factors on overall flood risk facilitate municipalities facing unique urban flooding challenges arising from sediment accumulation during long dry periods, followed by short-duration, high-intensity rainfall. Full article

Predictive Maintenance (PdM) approach in Combined Sewer Systems (CSS) is gaining momentum due to advances in sensor technology, affordability and availability of data, and the rise of machine learning and data analytics. This study aims to characterize the general behavior of CSS under Dry and Wet weather conditions. To achieve this, 10 min resolution precipitation and water level data were collected from nearby SIAS stations and AMAP radar water level sensors, installed at the outlet chamber of the CSS, respectively. Precipitation data was used to segment continuous time series data into Dry Weather Flow (DWF) and Wet Weather Flow (WWF). DWF analysis exhibited unique flow patterns that strongly correlated with water consumption behaviors of households. For wet weather, a comparison was made between key rainfall parameters (depth, intensity) and peak water level data, and nonlinear relationships were observed that highlight the complex rainfall–runoff process. These findings underscore the need for separate predictive models tailored to DWF and WWF characteristics. Integrating high-resolution sensor data with machine learning models such as Long Short-Term Memory (LSTM) networks and anomaly detection, Autoencoders can enhance PdM, improving CSS management and reducing risks of blockage events and infrastructure failures. Full article

Assessing hydropower potential in ungauged basins requires consistent derivation of key hydrological variables from heterogeneous geospatial and climatic data. Conventional GIS-based approaches often rely on fragmented, user-dependent workflows, limiting reproducibility and comparability. This study presents HYDROPOT, a web-based geospatial framework for the automated and reproducible extraction of hydrologically relevant basin descriptors for regional-scale hydropower screening. The platform integrates centralized datasets with server-side geoprocessing to delineate upstream catchments and compute quantitative basin descriptors, including drainage area (2–400 km²), Curve Number (CN), concentration time, and spatially aggregated monthly thermo-pluviometric variables derived from 95 stations over the 2004–2022 period. These descriptors provide essential inputs for rainfall–runoff modeling and preliminary discharge estimation, thereby supporting (although not directly performing) the assessment of water availability in ungauged basins. By eliminating manual preprocessing, HYDROPOT ensures consistent and reproducible analyses, reducing user-induced variability and improving comparability across applications, without implying increased predictive accuracy. The framework, applied to the Lazio Region (Central Italy) over the 2004–2022 period, enables rapid and transparent screening of river reaches, offering a scalable decision-support tool for preliminary, input-based screening in early-stage small hydropower planning. Full article

Floods increasingly threaten communities and infrastructure in Uganda due to climate variability and land use changes. This study assessed flood hazard, vulnerability, and risk in the Mpanga River Catchment using the Rainfall–Runoff–Inundation (RRI) model integrated with the Analytical Hierarchy Process (AHP). The RRI model showed good performance during calibration (NSE = 0.83) and validation (NSE = 0.71), enabling the generation of hazard maps for different return periods. Results revealed a clear escalation in flood extent with increasing return period, where inundation expanded from about 120.5 km² in the 5-year event to nearly 348.4 km² under the 100-year flood scenario. Vulnerability was evaluated through AHP using nine indicators (Land use, population density, distance to river, elevation, rainfall, slope, drainage density, Total Wetness Index, and soil type); however, only Land Use and population density were retained in the final mapping due to data relevance and weight dominance. Combining hazard and vulnerability produced risk maps that revealed most of the catchment falls under low to moderate risk, with high-risk areas concentrated in upstream urbanized zones. Validation with satellite-derived flood maps confirmed model reliability. Evaluation of mitigation strategies showed dams and channel improvements to be the most effective in reducing flood extent. The study provides a practical framework for flood risk management in data-scarce environments, supporting evidence-based planning and interventions. Full article

Rain-on-snow (ROS) is a hydrometeorological phenomenon in which liquid precipitation falls onto an existing snowpack, augmenting runoff through the combined effects of rainfall and accelerated snowmelt. Anthropogenic climate change is progressively shifting the rain-to-snow partitioning of precipitation and altering land-surface conditions across mid- to high-latitude mountainous regions, thereby heightening flood potential. Most previous work, however, has addressed ROS at regional scales and over historical periods; hemispheric-scale assessments of future ROS dynamics and their implications for flood hazard and societal exposure remain scarce. Here we apply 10 bias-corrected CMIP6 models together with ERA5-Land reanalysis data to project changes in ROS days across the Northern Hemisphere under four Shared Socioeconomic Pathway (SSP) scenarios. ROS days are coupled with flood frequency analysis to quantify changes in ROS flood occurrence, and gridded population and Gross Domestic Product (GDP) data are integrated to evaluate future population-socioeconomic exposure. Under low-to-medium emission scenarios, ROS days increase substantially over historical hotspots, whereas under high-emission scenarios they decline at mid- to high latitudes yet expand into previously unaffected high-latitude and inland cold regions. ROS flood days respond nonlinearly to ROS frequency because progressive snow water equivalent loss limits runoff generation, causing ROS floods to decrease in some mountainous areas even as ROS events become more frequent. Population-socioeconomic exposure exhibits a corresponding polarization: it declines in mid-latitude regions where snow cover is disappearing but rises sharply at high latitudes, with high-emission pathways accelerating the northward migration of disaster risk. These findings bridge critical gaps in large-scale ROS climatology and shed light on future changes in ROS-induced hydrological extremes. Besides, the findings facilitate the creation of regionally focused adaptation strategies and provide useful references for integrating climate model projections with remote sensing observations to improve future monitoring and risk assessment of ROS-related floods. Full article

Tropical coastal regions exhibit pronounced spatial and temporal variability in rainfall driven by seasonal atmospheric circulation and coastal–orographic interactions. Accurate representation of the temporal distribution of rainfall is essential for hydrologic modeling and infrastructure design. This study develops regionalized Huff curves for the Department of Magdalena, Colombia, addressing a critical gap in the characterization of rainfall temporal patterns in tropical coastal regions. A total of 270 short-duration (5–6 h) rainfall events from automatic stations were converted into normalized cumulative mass curves. The resulting curves were grouped into homogeneous temporal patterns using clustering algorithms. Three dominant storm types were identified: early-peak (Curve 1), intermediate (Curve 2), and uniform (Curve 3), reflecting the region’s coastal, lowland, and orographic influences. Probability envelopes and representative design hyetographs were derived to quantify intra-event variability. Rainfall–runoff simulations for a 100-km² watershed showed peak-flow differences of up to 132% between storm types, highlighting the sensitivity of hydrologic response to rainfall temporal distributions. The resulting regionalized Huff curves provide a practical and transferable framework for hydrologic modeling, flood-risk assessment, and infrastructure planning in tropical regions with limited high-resolution rainfall data. Full article

Rainfall-induced erosion poses a serious threat to river levee slopes, where raindrop impact and surface runoff trigger particle detachment, rill initiation, and gully development, leading to rapid soil loss and local instability. This study experimentally evaluated short-fiber reinforcement as an erosion-control measure for levee slopes under controlled rainfall conditions. Laboratory embankment models were constructed using a uniform soil mixture and compacted under consistent moisture conditions. Simulated rainfall was applied at intensities of 50 and 100 mm/h. Erosion progression was monitored through time-series observations and quantified using sediment collection and three-dimensional surface measurements. Comparative tests were performed on unreinforced and fiber-reinforced slopes to examine the influence of fiber bridging and surface anchoring on the initiation and development of erosion. The results showed that short-fiber reinforcement delayed rill formation and reduced soil loss. Under 50 mm/h rainfall, 1% coir fiber reduced the eroded mass by approximately 70%, whereas polypropylene fiber achieved approximately 42% reduction compared with the unreinforced control. These findings suggest that short natural fibers can effectively enhance the erosion resistance of compacted levee slopes under rain. Full article

The integration of remote sensors with nature-based solutions (NBS) offers new opportunities for suitable hydrological monitoring and risk reduction. This study implements 2D-1D numerical modelling tools to guide the allocation of both sensors and NBS interventions. Using the Cerisano catchment as a case study, the software IBER-SWMM (3.3.1–5.2 respectively) was employed to reproduce rainfall-runoff under different scenarios. Outputs were analysed to identify hydrologically sensitive zones where sensor deployment and NBS implementation would maximise monitoring efficiency and mitigation benefits. Results demonstrate how modelling supports effective decisions that are often limited by budget and site-specific constraints. Full article

To further investigate stormwater runoff patterns, pathogenic risks of pollutants on urban roads, and mitigation of urban non-point source pollution, road runoff monitoring and sampling were conducted in selected sections of central Yinchuan, a city in the arid region of northwestern China. Processed data—including rainfall, flow rate, and water quality parameters (conventional five indicators and heavy metals)—were obtained from ten rainfall events in 2024. Through analyses of water quality characteristics, influencing factors, runoff flushing patterns, and stormwater control measures, the current status of road runoff pollution was clarified. The Nemerow pollution index method was applied to evaluate pollutant levels and assess human health risks. Results indicate that pollution levels in Yinchuan are relatively mild, with most pollutant concentrations below the Class IV surface water quality standard. Basic rainfall parameters—peak rainfall intensity (PRI), average rainfall intensity (ARI), and previous sunny days (PSD)—together with urban functional zones, significantly influence pollutants in rainfall runoff, with the antecedent dry period showing the most pronounced effect. Analysis of the runoff scouring effect reveals that scouring of the conventional five water quality indicators (SS, COD, TN, NH₃-N, and TP) is substantially more evident than that of heavy metals. The runoff control depth for roads in central Yinchuan ranges from 0.9 mm to 40 mm, sufficient to manage runoff pollution from small to medium-sized rainfall events. The Nemerow pollution index remains below 8.36, with no severely polluted areas identified, indicating relatively low pollution in Yinchuan’s urban core. Quantitative human health risk assessment suggests that health risks associated with heavy metals on roads are low, with no significant exposure risk, implying that stormwater runoff in Yinchuan poses no substantial threat to human health. This study provides a valuable reference for non-point source pollution control via stormwater runoff management in arid-region cities. Full article

This study proposes a comprehensive methodology for the design and performance assessment of infiltration ponds integrated within hybrid grey–green urban drainage systems. The scope of the ponds is twofold: (i) increase infiltration of rainwater, and hence groundwater recharge, and (ii) decrease pluvial discharge downstream. The framework is applied to the Rome Technopole district, which serves as a pilot case for testing and demonstrating the procedure. Through 30-year continuous simulations performed with the EPA Storm Water Management Model and forced with a 5 min historical rainfall, the approach enables a performance-based evaluation that captures the full hydrological variability and the hydraulic performances of urban drainage systems. The methodology relies on physically based models for both the grey stormwater drainage network and the infiltration ponds, combined with a long-term simulation and functional analysis under transient conditions. The approach explicitly represents the main hydrological processes, including runoff generation, flow routing, storage dynamics, infiltration, and soil moisture variability, enabling a quantitative evaluation of peak-flow attenuation, infiltration efficiency, groundwater recharge volumes, seasonal variability, and wet–dry cycle behaviour. The latter is used to assess the long-term evolution of pond performance and its implications for maintenance activities, including clogging development and removal. Scenario analyses explore the influence of pond geometry and storage volumes, highlighting the trade-offs between hydrological efficiency, evaporation losses, and drawdown times. Beyond the specific application to the Rome Technopole developed in this study, we propose a generalizable, practitioner-oriented design procedure suited to contexts where infiltration-based solutions are desirable but regulatory guidance is fragmented. The proposed design workflow identifies critical parameters for both the hydraulic design and the operational management of infiltration ponds, enabling a statistical evaluation of their performance. The analysis of peak-flow reduction, infiltrated volumes, and the timing and frequency of wet–dry cycles provides a robust technical basis for the proper sizing, integration, and long-term assessment of infiltration ponds within urban drainage planning. Full article

Accurate forecasting of urban rainfall-runoff pollution across large river basins is essential for urban water management. However, this task faces formidable challenges due to the scarcity of locally monitored data and the heterogeneity in hydrological and pollution processes. To address these challenges, we proposed a novel three-tiered framework comprising (1) functional area clustering using 16-dimensional features to identify zones with shared pollution mechanisms and establish a physical parameter library; (2) a hybrid physics-informed data-driven model integrating SWMM with a Residual-BiLSTM-Multi-Head Attention (RLA) model; and (3) cluster-based transfer learning enabling predictions in data-scarce zones. The framework’s efficacy was demonstrated through a multi-tiered dataset for the Yangtze River Basin. First, a knowledge base comprising 2390 reported rainfall events across 57 functional areas was synthesized to inform the functional clustering and establish a shared physical parameter library. Subsequently, intensive field monitoring from two representative residential areas was used to train and validate the hybrid model. In data-rich zones within a cluster, the model achieved high accuracy (R² > 0.82). For data-scarce zones within the same functional cluster, the model maintained a promising performance (R² > 0.5). This study presents a novel basin-scale framework, with its initial application and preliminary validation in the Yangtze River Basin. Full article

Single-species hedges can help mitigate a range of urban and climate change-related issues, such as slowing stormwater flow and reducing rainfall runoff, particularly during the growing season. There is, however, little information on the service delivery of mixed hedges and their comparison to single-species, year-round, as well as on the practicality of functional rather than ornamental plant mixing. Here, we report on an initial case study to address this. Chosen hedge taxa (Crataegus monogyna, Elaeagnus × submacrophylla ‘Gilt Edge’, Ligustrum ovalifolium, Thuja plicata ‘Atrovirens’) represented a range of plant characteristics. These were trialled outdoors in Reading (SE England, UK) as treatment groupings of either single-species or mixed-species (‘evergreen’ and ‘broadleaf’ mix), along with a bare soil control, in 110 L troughs. We applied 5 min simulated rainfall onto each treatment twice in every meteorological season and assessed canopy throughfall. We also monitored substrate moisture content change as a proxy for evapotranspiration and substrate storage capacity of subsequent rainfall. During summer, the deciduous taxa and mixed hedges had the highest evapotranspiration rates, suggesting their potential to influence soil water storage, but in our experimental setup, that did not translate into significant differences in substrate moisture between treatments. During autumn and winter, the single-species Thuja treatment had the highest rainfall interception rate, followed by both mixed species treatments. In winter, canopy and leaf characteristics rather than physiological activity correlated with increased rainfall attenuation. However, by the end of the experiment (spring 2023), Crataegus, Thuja and both mixed hedge treatments had significantly lower throughfall (higher interception) compared to bare soil. We are continuing to test these treatments in a longer-term field experiment. Management of mixed-species hedges for rainfall attenuation is practically achievable, despite some differences in individual species’ growth rates and plant habits. Full article