Search Results (102)

Comprehension of spatio-temporal groundwater recharge (GWR) under climate change is imperative to enhance water resources availability and management. The main aim of this study is to examine climate change’s effects on spatio-temporal GWR. This study was done by ensembling five climate models and the physically-based WetSpass-M model to estimate GWR during baseline (1986 to 2015), mid-term (2031 to 2060), and long-term (2071 to 2100) periods for the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. In comparison to the Identification of unit Hydrographs and Component flows from Rainfall, Evaporation, and Streamflow (IHACRES)’s baseflow and direct runoff with corresponding WetSpass-M model outputs, the statistical indices showed good performance in simulating water balance components. Projected future temperature and rainfall will likely increase dramatically compared to the baseline period for RCP4.5 and RCP8.5. In comparison to the baseline period, the annual GWR had been projected to increase by 4.28 mm for RCP4.5 for the mid-term (MidT4.5), 15.27 mm for the long-term (LongT4.5), 2.38 mm for the mid-term (MidT8.5), and 13.11 mm for the long-term for RCP8.5 (LongT8.5), respectively. The seasonal GWR findings showed an increasing pattern during winter and spring, whereas it declined in autumn and summer. The mean monthly GWR for MidT4.5, LongT4.5, MidT8.5, and LongT8.5 will increase by 0.34, 1.26, 0.18, and 1.07 mm, respectively. The watershed’s downstream areas were receiving the lowest amount of GWR, and prone to drought. Therefore, this study advocates and recommends that stakeholders participate intensively in developing and implementing climate change resilience initiatives and water resources management strategies to offset the detrimental effects in the downstream 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

This study provides a comprehensive overview of watershed hydrology and mathematical models, focusing on its hydrological features and the implementation of hydrological modeling for effective water resource modeling and assessment, planning, and management. The study presents a thorough review of the primary transport mechanisms of water within a watershed, particularly the river network, and examines its physical and stochastic characteristics. It also discusses the derivation of governing equations for various hydrological processes within a watershed, including evaluating their applicability in the context of watershed modeling. Additionally, this research reviews the generation of hydrologic flux from rainfall events within a watershed and its subsequent routing through overland flow and channel networks. Furthermore, the study examines commonly utilized statistical distributions and methods in watershed hydrology, emphasizing their implications for watershed modeling. Finally, this research evaluates various mathematical models used in watershed processes modeling, highlighting their respective advantages and disadvantages in the context of water resource management studies. Full article

Rainfall-based methods have been employed for computing hydrographs in urban drainage systems. However, their implementation often introduces uncertainty in various aspects, such as the selection of a unit hydrograph, the choice of abstraction methods, and the formulas used to calculate the time of concentration, among others. Conventional consultancy studies tend to oversimplify catchment representation by treating it as a homogeneous unit or discretizing it into a few segments with simplified flood routing. This research proposes a streamlined methodology for computing hydrographs, considering the sub-basins’ heterogeneity. The methodology is based on the principles of proportionality and superposition. A sensitivity analysis of the proposed methodology is conducted, considering both homogeneous and heterogeneous catchments and the temporal distribution of rainfall. The proposed methodology is applied to the catchment of the Ricaurte channel, located in Cartagena de Indias (Colombia), with a watershed area of 728.8 ha. It has proven effective in representing a recorded simultaneous rainfall-runoff event, achieving a Root Mean Square Error of 3.93% in estimating the total volume of the measured hydrographs. A key advantage of the methodology, compared to traditional rainfall–runoff approaches, is that it does not require an extensive number of parameters to be calibrated. It may be utilized to estimate extreme flood events in urban areas with limited data availability, relying on minimal data inputs. Full article

Accurate modeling of pollutant transport during storm events is critical for watershed management and pollution mitigation. This study extends Diskin’s Cell Model, originally developed for rainfall–runoff simulations, to incorporate pollutant transport dynamics. By integrating an Instantaneous Unit Hydrograph (IUH), the model transforms pollutant loads into effective mass transport predictions while ensuring mass conservation. The framework accounts for contamination mobilized by rainfall, including agricultural runoff and industrial discharges, and applies convolution-based routing to capture pollutant dispersion. Calibrations using single-cell, two-cell, and fifteen-cell watersheds validate the model’s predictive capability and demonstrate its effectiveness in estimating pollutant accumulation at downstream locations. The results highlight the model’s potential for scalable water quality assessments, stormwater pollution control, and data-driven watershed management strategies. Full article

The oceans remain one of Earth’s last great unknowns, with about 74% still unmapped to modern standards. Consequently, interpolation is employed to create seamless digital bathymetric models (DBMs) from incomplete hydrographic datasets, but this introduces unquantified depth uncertainties. This study aims to estimate and characterize uncertainties arising from set-line spacing hydrographic surveys, which are important for nautical charting, navigational safety, and many other applications. By sampling four distinct complete-coverage testbeds in United States waters that vary in slope and roughness at different line spacings, this study interpolates across entire testbed areas using Spline, Inverse Distance Weighting, and Linear interpolation. Uncertainty is calculated by comparing interpolated depths against the source depths for independent points. The resulting interpolation uncertainties are evaluated from both scientific and operational perspectives. Linear regression and machine learning techniques, specifically artificial neural networks and random forest, are used to model the relationship between these uncertainties and three ancillary predictors (distance to the nearest known measurement, slope, and roughness) for interpolation uncertainty quantification. The results show operational equivalence among the three interpolators, how line spacing and morphology impact uncertainty, and the statistical significance of the examined uncertainty predictors. However, the relationships between the combined ancillary predictors and interpolation uncertainty are weak. These findings suggest the potential presence of unaccounted-for factors influencing uncertainty yet provide a foundational understanding for improving uncertainty estimates in DBMs within operational settings. Full article

This study critically examines the applications of the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) in hydrological research from 2000 to 2023, with a focus on its use in event-based and continuous simulations. A bibliometric analysis reveals a steady growth in research productivity and identifies key thematic areas, including hydrologic modeling, climate change impact assessment, and land use analysis. Event-based modeling, employing methods such as the SCS curve number (CN) and SCS unit hydrograph, demonstrates exceptional performance in simulating short-term hydrological responses, particularly in flood risk management and stormwater applications. In contrast, continuous modeling excels in capturing long-term processes, such as soil moisture dynamics and groundwater contributions, using methodologies like soil moisture accounting and linear reservoir baseflow approaches, which are critical for water resource planning and climate resilience studies. This review highlights the adaptability of HEC-HMS, showcasing its successful integration of event-based precision and continuous process modeling through hybrid approaches, enabling robust analyses across temporal scales. By synthesizing methodologies, performance metrics, and case studies, this study offers practical insights for selecting appropriate modeling techniques tailored to specific hydrological objectives. Moreover, it identifies critical research gaps, including the need for advanced calibration methods, enhanced parameter sensitivity analyses, and improved integration with hydraulic models. These findings highlight HEC-HMS’s critical role in improving hydrological research and give a thorough foundation for its use in addressing current water resource concerns. Full article

The general unit hydrograph (GUH), recently established by Guo, represents the most advanced hydrograph model today, but how to implement it with hydrologic data is another story. In this work, an effective initial value-based method for estimating the parameters in the GUH model is proposed and applied to the analysis of flood processes. In contrast to the flood-rainfall united fitting method, which heavily depends on the flood records and has a broad range of parameter variations, which makes it practically intractable, the initial value-based method enables the calculation of model parameters directly from the measured rainstorm data and greatly enriches the discharge dataset so that more accurate prediction of flood processes becomes achievable. From the data collected from several watersheds, we find that smaller-shape parameters usually indicate a multi-peak flood process, and the rainfall patterns have a significant impact on flood peaks. These results provide a reliable approach for the prediction of floods in streams with scarce discharge data. Additionally, it is observed that the peak time lags have a notable increase from the southwest to the northeast of Zhejiang. Full article

Designing floods in ungauged watersheds with limited data is a significant challenge in water conservancy projects. To address this, the method of calculating the design flood peak and flood volume using the weighted average method was proposed, which is based on the instantaneous unit hydrograph method and the inference formula method, combined with the characteristics of heavy rainfall floods in ungauged watersheds. The calculation results are analyzed in terms of reasonableness through the distribution pattern of the flood peak modulus under different frequencies of the constructed reservoirs, the relative error analysis, and the HEC-RAS model. Based on the one-day flood process of the adjacent basin, the calculation of deducing the design flood process using the hydrological comparison method was proposed. Taking the “Stormwater Runoff Chart” as the data source, the runoff generation, and concentration model was established with the design flood of Baludi Reservoir in the Gelangram River basin of Menglian, Yunnan Province as the research object. A comparative study of the results of the design floods calculated by different methods was carried out. The results show that the new method can well describe the rainstorm process. The method has better performance in the application to the design flood calculation of ungauged basins due to its consideration of the influence of subsurface conditions. The method not only reduces the construction cost but also improves the safety of the reservoir through a better-fitted design flood calculation. Full article

Synthetical eco-environmental problems’ treatment is a new stage for certain pollutant control or ecological restoration. Traditional urban planners have focused more on social–economic development but less on eco-environmental considerations. Spatial planning is currently an essential administrative management method for regional development and eco-environmental protection in China. National and provincial spatial planning designs general strategies, and prefecture-level planning is the most important scale for spatial management. For scientific, spatial governance for eco-environmental protection, we propose a synthetic spatial analysis and planning method framework that involves atmospheric, edaphic, hydrographic, and ecological processes to identify pivotal regions for regional eco-environmental security goals. The synthetic method was conducted using advanced models including the CMAQ and SWAT models and spatial statistical methods. A Chinese prefecture-level city, Anshan City, was chosen to fulfill the method framework due to its various ecosystem types and environmental problems. A total of 67 eco-environmental management units (EMU) were divided based on atmospheric pollution patterns, hydrographic processes, edaphic heavy metal pollution, and ecological spatial analysis. Each unit was identified with ecological or environmental risk and a proposed management regulation. For considering the whole eco-environmental process, the ecological security pattern (ESP) was constructed. The results showed that 166 corridors were identified with an area of 2241.25 km², with enhanced connectivity among 76 ecological sources (12.27% of Anshan City). By coupling two results, the optimized ecological conservation and restoration pattern was proposed, in which priority protection areas were identified. This synthetic method can provide scientific analysis and guidance to support spatial planning and ecological construction for multi-purpose ecological and environmental protection. Full article

In the vicinity of the Adamów power plant, which operates in the catchment area of the Kiełbaska river, there is a significant shortage of water resources caused by the intensive use of water by the energy industry and agriculture. The development of the plant by replacing the outdated coal-fired (lignite-fired) units with modern gas and steam units may contribute significantly to reducing the negative impact on the environment and reduce the demand for water resources relative to coal technology. Gas and steam units are a much more energy-efficient technology. This implies a lower demand for water, a reduction in pollutant emissions, and greater operational flexibility, which enables the units to adapt to changing hydrological and environmental conditions. The high efficiency of these units limits the need for frequent water-refilling, while allowing for a more sustainable and stable production of energy. Based on an analysis of hydrological data for the years 2019–2023, it was estimated that water stress is observed in this catchment area on 198 days per year, which accounts for c.a. 54% of the hydrological year. Therefore, it is assumed that inter-catchment pumping stations with a flow of 0.347 m³∙s⁻¹ will be required. This sets the demand for water at 5.95 million m³ per year. The planned water transfer will be carried out from Jeziorsko reservoir on the Warta river through the catchment area of Teleszyna river. Moreover, there are plans for the reconstruction of the layout of Kiełbaska Duża and Teleszyna rivers, which would involve the restoration of natural run-offs, following the discontinuation of open-pit lignite mining. This will additionally be supported by the reduced demand for water in the water use system when using the modernised power plant. The analysed data made it possible to develop hydrological scenarios that take the future reduction in water stress into account by implementing plans to restore the former hydrographic system in the region. These investments would also foresee the creation of new retention reservoirs (in former mining pits) with a capacity of nearly 900 million m³, which will significantly increase the region’s water resources and retention potential, supporting hydrological and energy security for the years to come. Full article

Many hydrological models incorporate vegetation-related parameters to describe hydrological processes more precisely. These parameters should adjust dynamically in response to seasonal changes in vegetation. However, due to limited information or methodological constraints, vegetation-related parameters in hydrological models are often treated as fixed values, which restricts model performance and hinders the accurate representation of hydrological responses to vegetation changes. To address this issue, a vegetation-related dynamic-parameter framework is applied on the Xinanjiang (XAJ) model, which is noted as Eco-XAJ. The dynamic-parameter framework establishes the regression between the Normalized Difference Vegetation Index (NDVI) and the evapotranspiration parameter K. Two routing methods are used in the models, i.e., the unit hydrograph (XAJ-UH and Eco-XAJ-UH) and the Linear Reservoir (XAJ-LR and Eco-XAJ-LR). The original XAJ model and the modified Eco-XAJ model are applied to the Ou River Basin, with detailed comparisons and analyses conducted under various scenarios. The results indicate that the Eco-XAJ model outperforms the original model in long-term discharge simulations, with the NSE increasing from 0.635 of XAJ-UH to 0.647 of Eco-XAJ-UH. The Eco-XAJ model also reduces overestimation and incorrect peak flow simulations during dry seasons, especially in the year 1991. In drought events, the modified model significantly enhances water balance performance. The Eco-XAJ-UH outperforms the XAJ-UH in 9 out of 16 drought events, while the Eco-XAJ-LR outperforms the XAJ-LR in 14 out of 16 drought events. The results demonstrate that the dynamic-parameter model, in regard to vegetation changes, offers more accurate simulations of hydrological processes across different scenarios, and its parameters have reasonable physical interpretations. Full article

As the climate crisis progresses, droughts and the seasonal availability of fresh water are becoming increasingly common in different regions of the world. One solution to tackle this problem is the reuse of treated wastewater in agriculture. This study was carried out in two significant hydrographic regions located in the southeast of Brazil (Mogi Guaçu River Water Management Unit—UGRHI-09 and Piracicaba River Basin—PRB) that have notable differences in terms of land use and land cover. The aim of this study was to carry out a multi-criteria analysis of a set of environmental attributes in order to classify the areas under study according to their levels of soil suitability and runoff potential. The integrated analysis made it possible to geospatialize prospective regions for reuse, under two specified conditions. In the UGRHI-09, condition 1 corresponds to 3373.24 km², while condition 2 comprises 286.07 km², located mainly in the north-western and central-eastern portions of the unit. In the PRB, condition 1 was also more expressive in occupational terms, corresponding to 1447.83 km²; and condition 2 was perceptible in 53.11 km², predominantly in the central region of the basin. The physical characteristics of the areas studied were decisive in delimiting the areas suitable for the reuse of treated wastewater. Full article

The Brazilian savanna (Cerrado Biome) is one of the most important regions in the world in terms of food production, with the use of fertilizers based on nitrogen, phosphorus and potassium (NPK). When not applied properly, fertilizers can alter and affect water quality. The objective of this study was to evaluate the presence of these compounds in surface and groundwater in the Upper Jardim River Hydrographic Unit, Federal District, thus characterizing seasonal variations during the dry and rainy seasons in two periods. A total of 207 groundwater samples and 23 surface water samples were collected in the years 2014, 2015, 2019 and 2020. The parameters analyzed were pH and nitrate, nitrite, ammonium, phosphate and potassium ions. In groundwater samples, pH values were significantly higher and ion levels lower in samples collected during the early years (except for nitrate), and the ammonium concentrations were lower in the dry season than the rainy (in 2014 and 2019). In surface samples, total phosphorus levels were significantly higher in the rainy/2019 compared to the rainy/2020 season, while this tendency was inverted for potassium during the dry season. The use of NPK-based fertilizers has increased considerably in recent years in the region due to the expansion of the agricultural area, and although the results of the study show that concentrations in water are much lower than the maximum values allowed by Brazilian legislation, continuous monitoring is necessary to guarantee water quality. Full article

This study develops a structure for mapping native vegetation in a transition area between the Brazilian Cerrado and the Atlantic Forest from integrated spatial information of Sentinel-1 and Sentinel-2 satellites. Most studies use integrated data to improve classification accuracy in adverse atmospheric conditions, in which optical data have many errors. However, this method can also improve classifications carried out in landscapes with favorable atmospheric conditions. The use of Sentinel-1 and Sentinel-2 data can increase the accuracy of mapping algorithms and facilitate visual interpretation during sampling by providing more parameters that can be explored to differentiate land use classes with complementary information, such as spectral, backscattering, polarimetry, and interferometry. The study area comprises the Lobo Reservoir Hydrographic Basin, which is part of an environmental conservation unit protected by Brazilian law and with significant human development. LULC were classified using the random forest deep learning algorithm. The classifying attributes were backscatter coefficients, polarimetric decomposition, and interferometric coherence for radar data (Sentinel-1), and optical spectral data, comprising bands in the red edge, near-infrared, and shortwave infrared (Sentinel-2). The attributes were evaluated in three settings: SAR and optical data in separately settings (C1 and C2, respectively) and in an integrated setting (C3). The study found greater accuracy for C3 (96.54%), an improvement of nearly 2% compared to C2 (94.78%) and more than 40% in relation to C1 (55.73%). The classification algorithm encountered significant challenges in identifying wetlands in C1, but performance improved in C3, enhancing differentiation by stratifying a greater number of classes during training and facilitating visual interpretation during sampling. Accordingly, the integrated use of SAR and optical data can improve LULC mapping in tropical regions where occurs biomes interface, as in the transitional Brazilian Cerrado and Atlantic Forest. Full article