Search Results (6,403)

Cyanobacterial blooms in urban rivers present critical ecological threats worldwide, yet their mechanisms in fluvial systems remain inadequately explored compared to lacustrine environments. This study addresses this gap by investigating bloom dynamics in the eutrophic Majiagou River (Harbin, China) through phytoplankton resource use efficiency (RUE), calculated as chlorophyll-a per unit TN/TP. Seasonal sampling (2022–2024) across 25 rural-to-urban sites revealed distinct spatiotemporal patterns: urban sections exhibited 1.9× higher cyanobacterial relative abundance (RAC, peaking at 40.65% in autumn) but 28–30% lower RUE than rural areas. Generalized additive models identified nonlinear RAC–RUE relationships with critical thresholds: in rural sections, RAC peaked at TN-RUE 40–45 and TP-RUE 25–30, whereas urban sections showed lower TN-RUE triggers (20–25) and suppressed dominance above TP-RUE 10. Seasonal extremes drove RUE maxima in summer and minima during freezing/thawing periods. These findings demonstrate that hydrological stagnation (e.g., river mouths) and pulsed nutrient inputs reduce nutrient conversion efficiency while lowering bloom-triggering thresholds under urban eutrophication. The study establishes RUE as a predictive indicator for bloom risk, advocating optimized N/P ratios coupled with flow restoration rather than mere nutrient reduction. This approach provides a science-based framework for sustainable management of urban river ecosystems facing climate and anthropogenic pressures. Full article

Urban flooding, intensified by climate change, poses significant threats to sustainable development, necessitating accurate precipitation projections for effective risk management. This study utilized Bayesian Model Averaging (BMA) to optimize CMIP6 multi-model ensemble precipitation projections for Shanghai, integrating Delta statistical downscaling with observational data to enhance spatial accuracy and reduce uncertainty. After downscaling, RMSE values of daily precipitation for individual models range from 10.158 to 12.512, with correlation coefficients between −0.009 and 0.0047. The BMA exhibits an RMSE of 8.105 and a correlation coefficient of 0.056, demonstrating better accuracy compared to individual models. The BMA-weighted projections, coupled with Soil Conservation Service Curve Number (SCS-CN) hydrological model and drainage capacity constraints, reveal spatiotemporal flood risk patterns under Shared Socioeconomic Pathway (SSP) 245 and SSP585 scenarios. Key findings indicate that while SSP245 shows stable extreme precipitation intensity, SSP585 drives substantial increases—particularly for 50-year and 100-year return periods, with late 21st century maximums rising by 24.9% and 32.6%, respectively, compared to mid-century. Spatially, flood risk concentrates in peripheral districts due to higher precipitation exposure and average drainage capacity, contrasting with the lower-risk central urban core. This study establishes a watershed-based risk assessment framework linking climate projections directly to urban drainage planning, proposing differentiated strategies: green infrastructure for runoff reduction in high-risk areas, drainage system integration for vulnerable suburbs, and ecological restoration for coastal zones. This integrated methodology provides a replicable approach for climate-resilient urban flood management, demonstrating that effective adaptation requires scenario-specific spatial targeting. Full article

Urban areas face escalating hydrological risks due to climate change, urban sprawl, and aging stormwater infrastructures. In this context, Nature-Based Solutions (NbSs), especially Sustainable Urban Drainage Systems (SUDSs), have emerged as viable strategies to enhance water resilience and sustainability. However, the literature still lacks standardized and scalable methodologies for their design and performance monitoring. This study conducts a systematic review following the PRISMA protocol, combined with bibliometric and co-occurrence analyses, to identify prevailing approaches in the sizing and monitoring of NbS-based SUDSs. Based on the peer-reviewed literature indexed in Scopus and Web of Science from 2020 to 2024, the findings reveal an increasing integration of hydrological modeling with artificial intelligence, remote sensing, and IoT-based real-time monitoring. Despite this progress, challenges remain in methodology validation, data availability, and system adaptability. The review underscores the need for hybrid, context-sensitive frameworks that integrate empirical and simulated data to support decision-making in urban drainage planning and management. Full article

In recent years, the irregular wave characteristics of ocean dynamics have often been overlooked in the study of the driving mechanism of groundwater movement in coastal aquifers. To clarify the propagation mechanisms of groundwater fluctuations driven by irregular waves in beach aquifers, we employed spectral analysis based on numerical simulations to examine the energy migration processes and evolution characteristics of wave signals at different frequencies. It elucidates the response mechanism of groundwater movement characteristics (head, velocity) to irregular waves in the sea. The energy density in the low-frequency region is enhanced compared to the incident wave and continuously increases in the direction away from the sea within the aquifer. The wavelet power corresponding to the 1/2 spectral peak frequency is significantly enhanced. The energy density in the high-frequency region is generally weaker than that of the incident waves, and the wavelet power corresponding to double spectral peak frequency is enhanced. The correlation between incident waves and groundwater fluctuations is highest near the spectral peak period. This study addresses some problems in modeling surface water–groundwater interactions under irregular wave conditions and provides a theoretical reference for investigating the impacts of extreme climate events (such as typhoon waves and low-frequency offshore oscillations generated by storm surges) on seawater intrusion into coastal groundwater systems. Full article

This paper introduces a methodology for forecasting spring hydrographs based on projections from regional climate models. The primary study objective was to evaluate how climate change may affect spring discharge. A statistical–analytical modeling approach was developed and applied to the Jósva spring catchment in the Aggtelek Karst region of Hungary. Historical data served to establish a regression relationship between rainfall and peak discharge. This approach is particularly useful for predicting discharge in cases where only historical rainfall data are available for calibration. Baseflow recession was analyzed using a two-component exponential model, with hydrograph decompositionand parameter optimization performed on the master recession curve. Future discharge time series were generated using rainfall data from two selected regional climate model scenarios. Both scenarios suggest a decline in baseflow discharge during different periods of the 21st century. The findings indicate that climate change is likely to intensify hydrological extremes in the coming decades, irrespective of whether moderate or high CO₂ emission scenarios unfold. Full article

Mid–long-term streamflow prediction (MLSP) plays a critical role in water resource planning amid growing hydroclimatic and anthropogenic uncertainties. Although AI-based models have demonstrated strong performance in MLSP, their capacity to quantify predictive uncertainty remains limited. To address this challenge, a DeepAR-based probabilistic modeling framework is developed, enabling direct estimation of streamflow distribution parameters and flexible selection of output distributions. The framework is applied to two case studies with distinct hydrological characteristics, where combinations of recurrent model structures (GRU and LSTM) and output distributions (Normal, Student’s t, and Gamma) are systematically evaluated. The results indicate that the choice of output distribution is the most critical factor for predictive performance. The Gamma distribution consistently outperformed those using Normal and Student’s t distributions, due to its ability to better capture the skewed, non-negative nature of streamflow data. Notably, the magnitude of performance gain from using the Gamma distribution is itself region-dependent, proving more significant in the basin with higher streamflow skewness. For instance, in the more skewed Upper Wudongde Reservoir area, the model using LSTM structure and Gamma distribution reduces RMSE by over 27% compared to its Normal-distribution counterpart (from 1407.77 m³/s to 1016.54 m³/s). Furthermore, the Gamma-based models yield superior probabilistic forecasts, achieving not only lower CRPS values but also a more effective balance between high reliability (PICP) and forecast sharpness (MPIW). In contrast, the relative performance between GRU and LSTM architectures was found to be less significant and inconsistent across the different basins. These findings highlight that the DeepAR-based framework delivers consistent enhancement in forecasting accuracy by prioritizing the selection of a physically plausible output distribution, thereby providing stronger and more reliable support for practical applications. Full article

This study examines the impact of catchment characteristics and design on the performance of urban lakes in terms of water quality and stormwater harvesting potential. Two urban lake systems in Western Sydney, Australia, were selected for comparison: Wattle Grove Lake, a standalone constructed lake, and Woodcroft Lake, part of an integrated wetland–lake system. Both systems receive runoff from surrounding residential catchments of differing sizes and land uses. Over a one-year period, continuous monitoring was conducted to evaluate water quality parameters, including turbidity, total suspended solids (TSS), nutrients (total nitrogen and total phosphorus), pH, dissolved oxygen, and biochemical oxygen demand. The results reveal that the lake with an integrated wetland significantly outperformed the standalone lake in terms of water quality, particularly in terms of turbidity and total suspended solids (TSS), achieving up to 70% reduction in TSS at the outlet compared to the inlet. The wetland served as an effective pre-treatment system, reducing pollutant loads before water entered the lake. Despite this, nutrient concentrations in both systems remained above the thresholds set by the Australian and New Zealand Environment and Conservation Council (ANZECC) Guidelines (2000), indicating persistent challenges in nutrient retention. Notably, the larger catchment area and shallow depth of Wattle Grove Lake likely contributed to higher turbidity and nutrient levels, resulting from sediment resuspension and algal growth. Hydrological modelling using the Model for Urban Stormwater Improvement Conceptualisation (MUSIC) software (version 6) complemented the field data and highlighted the influence of catchment size, hydraulic retention time, and lake depth on pollutant removal efficiency. While both systems serve important environmental and recreational functions, the integrated wetland–lake system at Woodcroft demonstrated greater potential for safe stormwater harvesting and reuse within urban settings. The findings from the study offer practical insights for urban stormwater management and inform future designs that enhance resilience and water reuse potential in growing cities. Full article

Global climate change, combined with the construction of impermeable urban elements, tends to increase runoff, which might cause flooding and reduce groundwater recharge. Moreover, the first flash of these areas might accumulate pollutants that might deteriorate groundwater quality. A digital elevation model (DEM) describes urban landscapes by representing the watershed relief at any given location. While, in concept, finer DEMs and land use classification (LUC) are yielding better hydrological models, it is suggested that over-accuracy overestimates minor tributaries that might be redundant. Optimal DEM resolution with integrated spectral and feature-based LUC was found to reflect the hydrological network’s significant tributaries. To cope with the karstic urban watershed complexity, ModClark Transform and SCS Curve Number methods were integrated over a GIS-HEC-HMS platform to a nominal urban watershed sub-basin analysis procedure, allowing for detailed urban runoff modeling. This precise urban karstic terrain modeling procedure can predict runoff volume and discharge in urban, mountainous karstic watersheds, and may be used for water-sensitive design or in such cities to control runoff and prevent its negative impacts. Full article

The estimation of soil hydraulic properties—such as water retention and hydraulic conductivity—is essential for irrigation management and agro-hydrological modeling. This study presents the development and application of SOILHP, a low-cost, IoT-integrated device designed to monitor laboratory evaporation experiments for the estimation of soil hydraulic properties using inverse modeling tools. SOILHP incorporates mini-tensiometers, a precision balance, microcontrollers, and cloud-based data logging via Google Sheets. SOILHP enables the remote, real-time acquisition of soil pressure head and mass variation data without the need for commercial dataloggers. Evaporation experiments were conducted using undisturbed soil samples, and inverse modeling with Hydrus-1D was used to estimate van Genuchten–Mualem parameters. The optimized parameters showed low standard errors and narrow 95% confidence intervals, demonstrating the robustness of the inverse solution, confirming the device’s sensors accuracy. Forward simulations of internal drainage were performed to estimate the field capacity under different drainage flux criteria. The field capacity results aligned with values reported in the literature for tropical soils. Overall, SOILHP proved to be a reliable and economically accessible alternative for monitoring evaporation experiments aimed at fitting parameters of analytical functions that describe water retention and hydraulic conductivity properties within the soil pressure head range relevant to agriculture. Full article

Efficient surface water allocation in reservoir-equipped basins is essential for balancing competing demands within the Water–Energy–Food (WEF) nexus. This study investigated the applicability of Particle Swarm Optimization (PSO) for optimizing water distribution in the Machángara River Basin, Ecuador; a complex, constraint-rich hydrological system. Implemented via the Pymoo package in Python, the PSO model was evaluated across calibration, validation, and execution phases, and benchmarked against exact methods, including Linear Programming (LP) and Mixed Integer Linear Programming (MILP). The results revealed that standard PSO struggled to satisfy equality constraints and yielded suboptimal solutions, with elevated penalty costs. Despite incorporating MILP-inspired encoding and repair functions, the algorithm failed to identify feasible solutions that met operational requirements. The execution phase, which includes reservoir construction decisions, resulted in a total penalty exceeding EUR 164.95 billion, with no improvement observed from adding reservoirs. Comparative analysis confirmed that LP and MILP outperformed PSO in constraint compliance and penalty minimization. Nonetheless, the study contributes a reproducible implementation framework and a comprehensive benchmarking strategy, including synthetic test functions, performance metrics, and diagnostic visualizations. These tools can facilitate systematic evaluation of PSO’s behavior in high-dimensional, nonlinear environments and provide a foundation for future hybrid or adaptive heuristic models. The findings underscore the limitations of standard PSO in hydrological optimization but also highlight its potential when enhanced through hybridization. Future research should explore PSO variants that integrate exact solvers, adaptive control mechanisms, or cooperative search strategies to improve feasibility and convergence. This work advances the methodological understanding of metaheuristics in environmental resource management and supports the development of robust optimization tools under the WEF-nexus paradigm. Full article

China, facing severe saline–alkali land degradation, is grappling with the paradox of technically adequate but systemically deficient land consolidation. In response to the existing evaluation system’s over-reliance on physicochemical indicators and neglect of socioeconomic value, this study proposes the use of the Optimal Land Use Value (OLV) to construct a comprehensive benefit evaluation indicator system for saline–alkali land consolidation that encompasses ecosystem resilience, supply–demand balancing, and common prosperity. Considering a case project implemented from 2019 to 2022 in the Western Songnen Plain of China—one of the world’s most severely affected soda saline–alkali regions—this study combines the land use transition matrix with a comprehensive evaluation model to systematically assess the effectiveness and sustainability of land consolidation. The results reveal systemic deficiencies: within ecological spaces, short-term desalination succeeds but pH and organic matter improvements remain inadequate, while ecosystem vulnerability increases due to climate fluctuations and grassland conversion. In production spaces, cropland expansion and saline land reduction are effective, but water resource management proves unsustainable. Living spaces show improved infrastructure and income but face threats due to economic simplification and intergenerational unsustainability. For the investigated case, recommendations include shifting from technical restoration to systemic governance via three strategies: (1) biological–engineering synergy employing green manure to enhance soil microbial activity; (2) hydrological balancing through groundwater quotas and rainwater utilization; (3) specialty industry development for rural economic diversification. This study contributes empirical evidence on the conversion of saline–alkali land, as well as an evaluation framework of wider relevance for developing countries combating land degradation and pursuing rural revitalization. Full article

This study aims to enhance the aquatic eco-functional zoning by incorporating the spatial variability of hydrological processes during the zoning process. We propose a method for watershed eco-functional zoning based on distributed hydrological modeling. Using the Jinjiang Basin in Southeast China as a case study, we applied the Soil Water and Assessment Tool (SWAT) model to delineate the basic zoning units and simulate their hydrological processes. We integrated natural environmental indicators—specifically topography, vegetation, meteorology, and hydrology—with land use as a measure of human activity, while accounting for their spatial variability. This approach enabled us to conduct both first-level and second-level eco-functional zoning of the watershed. The results indicated that (1) the Jinjiang Basin can be categorized into three main groups consisting of six first-level aquatic ecological zones, which reflect the spatial variability of terrestrial natural environmental factors and their influence on aquatic ecosystems; (2) building on this categorization, the first-level aquatic ecological regions were further divided into five categories comprising 18 second-level aquatic ecological functional zones, emphasizing the impact of human activities on aquatic ecosystems and their associated first-level ecological service functions; and (3) the application of hydrological simulation techniques allows for a comprehensive assessment of the spatial variability of hydrological processes, thereby enhancing the validity of the ecological function zoning results and providing robust technical support for watershed ecological function zoning. 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

Water masses are continuously redistributing across the Earth, so accurately estimating their availability is essential. Global Navigation Satellite Systems (GNSSs) have demonstrated potential for observing vertical deformations, which is partly driven by terrestrial water storage (TWS) variations. This capability has been used in hydrogeodesy to estimate TWS variations. However, GNSS data inversions are often ill-posed, requiring regularization for stable solutions. This study considers the Least Squares Collocation (LSC) statistical method as an alternative. LSC uses covariance functions to characterize observations, parameters, and their interdependence. By incorporating additional physical information into inverse models, LSC allows ill-posed problems stabilization. To assess LSC effectiveness, we apply it to observed and simulated GNSS vertical displacement on Haiti island. Hydrological signals are modeled using Global Land Data Assimilation (GLDAS) data. In sparse GNSS data regions, findings indicate poor agreement between TWS and hydrological input, with a Root-Mean-Square-Error (RMSE) of 115 kg/m², a correlation of 0.3, and a reduction of 73%. However, in dense simulated GNSS areas, TWS and hydrological input show strong agreement, with an RMSE of 41 kg/m², a correlation of 0.83, and a reduction of 92%. The results confirm LSC potentiality for assessing TWS changes and improving water quantification in dense GNSS station region. Full article

The exponential increase in global plastic production, reaching over 380 million tons in recent years, has exacerbated environmental problems, particularly in agriculture. Agricultural residues, such as hazel (Corylus avellana L.) pruning and plastic wastes, are underutilized resources that can be transformed via pyrolysis into biochar. This study focuses on copyrolyzed biochar produced from hazel biomass and polyethylene and aims to evaluate its effect on the water retention properties of degraded Alfisol. Van Genuchten’s hydrological model was used to analyze parameters such as rapid drainage pores, plant-available water pores, and air capacity (AC) under varying particle sizes (small and large) and application rates (1% and 5% w/w). The results revealed that fine particles at higher doses (5%), especially in P-5%-large and P-5%-small, considerably improved plant-available water retention, particularly within micropores and mesopores. Microstructural modifications induced during pyrolysis enhanced the water retention capabilities of biochar copyrolyzed with plastic. However, its effects on AC and pore connectivity warrant further investigation to assess long-term soil functionality. By integrating waste valorization with improved agricultural practices, this study underscores the potential of biochar copyrolyzed with plastic as an amendment for degraded soil. However, the long-term stability of this amendment requires further study. Full article