Search Results (1,226)

Extreme precipitation events severely impact agriculture, reducing yields and land use efficiency. The spatiotemporal distribution of Total Column Water Vapor (TCWV), the primary gaseous form of water, directly influences sustainable agricultural management. This study, through multi-source data fusion, employs methods including the Mann–Kendall test, sliding change-point detection, wavelet transform, pixel-scale trend estimation, and linear regression to analyze the spatiotemporal dynamics of global TCWV from 1959 to 2023 and its impacts on agricultural systems, surpassing the limitations of single-method approaches. Results reveal a global TCWV increase of 0.0168 kg/m²/year from 1959–2023, with a pivotal shift in 2002 amplifying changes, notably in tropical regions (e.g., Amazon, Congo Basins, Southeast Asia) where cumulative increases exceeded 2 kg/m² since 2000, while mid-to-high latitudes remained stable and polar regions showed minimal content. These dynamics escalate weather risks, impacting sustainable agricultural management with irrigation and crop adaptation. To enhance prediction accuracy, we propose a novel hybrid model combining wavelet transform with LSTM, TCN, and GRU deep learning models, substantially improving multidimensional feature extraction and nonstationary trend capture. Comparative analysis shows that WT-TCN performs the best (MAE = 0.170, R² = 0.953), demonstrating its potential for addressing climate change uncertainties. These findings provide valuable applications for precision agriculture, sustainable water resource management, and disaster early warning. Full article

This study addresses the complex challenges associated with flexible mattress (soft mattress) installation in the sharply curved and deep-water sections of the Yangtze River, particularly in the Yaozui revetment reconstruction project. Under extreme hydrodynamic conditions—water depths exceeding 30 m and velocities over 2.5 m/s—the risk of structural failures such as displacement, flipping, or tearing of the mattress becomes significant. To improve construction safety and stability, the study integrates numerical modeling and on-site strain monitoring to analyze the mechanical response of flexible mattresses during deployment. A three-dimensional finite element model based on the catenary theory was developed to simulate stress distributions under varying flow velocities and angles, revealing stress concentrations at the mattress’s upper edge and reinforcement junctions. Concurrently, a real-time monitoring system using high-precision strain sensors was deployed on critical shipboard components, with collected data analyzed through a remote IoT platform. The results demonstrate strong correlations between mattress strain, flow velocity, and water depth, enabling the identification of high-risk operational thresholds. The proposed monitoring and early-warning framework offers a practical solution for managing construction risks in extreme riverine environments and contributes to the advancement of intelligent construction management for underwater revetment works. Full article

To study the generation and dissipation process of agglomerate fog in mountainous expressways and deeply understand the hazard mechanisms of agglomerate fog sections in mountainous expressways, based on the analysis of the geographical location characteristics of mountainous expressways and the spatial and temporal distribution characteristics of agglomerate fog, the airspace constitutive model of agglomerate fog in mountainous expressways was constructed based on Newton constitutive theory. Firstly, the properties of the Newtonian fluid and cluster fog were compared and analyzed, and the influence mechanism of environmental factors such as the altitude difference, topography, water system, valley effect, and vegetation on the generation and dissipation of agglomerate fog in mountainous expressways was analyzed. Based on Newton’s constitutive theory, the constitutive model of temperature, humidity, wind speed, and agglomerate fog points in the foggy airspace of the mountainous expressway was established. Then, the time and spatial distribution of fog in Chongqing and Guizhou from 2021 to 2023 were analyzed. Finally, the model was verified by using the meteorological data and fog warning data of Liupanshui City, Guizhou Province in 2023. The results show that the foggy airspace of mountainous expressways can be defined as “the space occupied by the agglomerate fog that occurs above the mountain expressway”; The temporal and spatial distribution of foggy airspace on expressways in mountainous areas is closely related to the topography, water system, vegetation distribution, and local microclimate formed by thermal radiation. The horizontal and vertical movements of the atmosphere have little influence on the foggy airspace on expressways in mountainous areas. The specific manifestation of time distribution is that the occurrence of agglomerate fog is concentrated from November to April of the following year, and the daily occurrence time is mainly concentrated between 4:00–8:00 and 18:00–22:00. The calculation results of the foggy airspace constitutive model of the expressway in the mountainous area show that when there is low surface radiation or no surface radiation, the fogging value range is [90, 100], and the fogging value range is [50, 70] when there is high surface radiation (>200), and there is generally no fog in other intervals. The research results can provide a theoretical basis for traffic safety management and control of mountainous expressway fog sections. Full article

Karst groundwater systems are critical to water supply and ecological sustainability in northern China, yet their heterogeneity poses challenges for flow characterization. The Yuquan Mountain (YM) Spring, historically a major karst spring in western Beijing, has experienced persistent drying, raising concerns about its recharge and flow mechanisms. This study integrates published isotope data, spatial distributions of Na⁺ and Cl⁻ as hydrochemical tracers, groundwater age estimates, and geophysical survey results to assess the recharge sources and flow pathways within the YM Spring catchment area. The analysis identifies two major recharge zones: the Tanzhesi area, primarily recharged by direct infiltration of precipitation through exposed carbonate rocks, and the Junzhuang area, which receives mixed recharge from rainfall and Yongding River seepage. Three potential flow pathways are proposed, including shallow flow along faults and strata, and a deeper, speculative route through the Jiulongshan-Xiangyu syncline. The synthesis of multiple lines of evidence leads to a refined conceptual model that illustrates how geological structures govern recharge, flow, and discharge processes in this karst system. These findings not only enhance the understanding of subsurface hydrodynamics in complex geological settings but also provide a scientific basis for future spring restoration planning and groundwater management strategies in the regions. Full article

For effective groundwater resource management, it is essential to model the dynamic behaviour of aquifers in response to rainfall. Here, a methodological approach using a recurrent neural network, specifically a Long Short-Term Memory (LSTM) network, is used to model groundwater levels of the shallow porous aquifer in Southern Italy. This aquifer is recharged by local rainfall, which exhibits minimal variation across the catchment in terms of volume and temporal distribution. To gain a deeper understanding of the complex interactions between precipitation and groundwater levels within the aquifer, we used water level data from six wells. Although these wells were not directly correlated in terms of individual measurements, they were geographically located within the same shallow aquifer and exhibited a similar hydrogeological response. The trained model uses two variables, rainfall and groundwater levels, which are usually easily available. This approach allowed the model, during the training phase, to capture the general relationships and common dynamics present across the different time series of wells. This methodology was employed despite the geographical distinctions between the wells within the aquifer and the variable duration of their observed time series (ranging from 27 to 45 years). The results obtained were significant: the global model, trained with the simultaneous integration of data from all six wells, not only led to superior performance metrics but also highlighted its remarkable generalization capability in representing the hydrogeological system. Full article

This paper presents the Green Watershed Index (GWI) methodology, focusing on the 17 sustainability indicators selected in the Razey watershed, NW Iran. Field surveys and data collection have provided the possibility of field inspection and measurement of the present condition of the watershed and the indicators taken. Based on the degree of compliance with the required process, each indicator was scored from 0 to 10 and classified into three categories: unsustainable, semi-sustainable, and sustainable. Using the Entropy method to assign weight to each indicator and formulating a proportional mathematical relationship, the GWI score for each sub-watershed was derived. Spatial changes regarding the selected indicators and, consequently, the GWI were detected in the study area. Development of water infrastructure, particularly in the upstream sub-watersheds, plays a great role in increasing the GWI score. The highest weight is related to environmental productivity (0.26), and the five indicators of water footprint, knowledge management and information quality system, landscape attractiveness, waste recycling, and corruption control have approximately zero weight due to their monotonous spatial distribution throughout sub-watersheds. Only sub-watershed R1 has the highest score (5.13), indicating a semi-sustainable condition. The rest of the sub-watersheds have unsustainable conditions (score below 5). Concerning the GWI, the watershed is facing a critical situation, necessitating the implementation of management and conservation strategies that align with the sustainability level of each sub-watershed. Full article

Optimizing hydrodynamic performance and dissolved oxygen (DO) distribution is essential for improving water quality management in industrial recirculating aquaculture systems. This study combines experimental measurements and data analysis to evaluate the effects of the inlet pipe flow rate (Q), deployment distance ratio (d/r), deployment angle (θ), inlet pipe structure on hydrodynamics and the dissolved oxygen distribution across various tank layers. The flow field distribution in the tanks was measured using Acoustic Doppler Velocimetry (ADV), and the hydrodynamic characteristics, including average velocity (v_avg) and the velocity uniformity coefficient (DU₅₀), were quantitatively analyzed. The dissolved oxygen content at different tank layers was recorded using an Aquameter GPS portable multi-parameter water quality analyzer. The findings indicate that average velocity (v_avg) and the velocity uniformity coefficient (DU₅₀) are key determinants of the hydrodynamic characteristic of circular aquaculture tanks. Optimal hydrodynamic performance occurs for the vertical single-pipe porous configuration at Q = 9 L/s, d/r = 1/4, and θ = 45°,the average velocity reached 0.0669 m/s, and the uniformity coefficients attained a maximum value of 40.4282. In a vertical single-pipe porous structure, the tank exhibits higher dissolved oxygen levels compared to a horizontal single-pipe single-hole structure. Under identical water inflow rates and deployment distance ratios, dissolved oxygen levels in the surface layer of the circular aquaculture tank are significantly greater than that in the bottom layer. The results of this study provide valuable insights for optimizing the engineering design of industrial circular aquaculture tanks and addressing the dissolved oxygen distribution across different water layers. Full article

Microplastic particles (MPs) are an emerging global pollutant of increasing concern due to their widespread occurrence, persistence, and multifaceted impact on aquatic ecosystems. This study provides a comprehensive review of peer-reviewed literature from 2011 to 2025, analysing the presence, distribution, and microbiological associations of MPs in surface waters across five continents. The findings confirm that MPs are present in both marine and freshwater systems, with concentrations varying by region, hydrology, and proximity to anthropogenic sources. Polyethylene and polypropylene were identified as the most common polymers, often enriched in river mouths, estuaries, and aquaculture zones. A key focus of this review is the plastisphere—microbial biofilms colonizing MPs—which includes both environmental and pathogenic bacteria such as Vibrio, Pseudomonas, and Acinetobacter. Notably, MPs serve as vectors for the spread of antibiotic resistance genes (ARGs), including sul1, tetA and ermF, and β-lactamase genes like blaCTX-M. This highlights their role in enhancing horizontal gene transfer and microbial dissemination. The results emphasize the need for standardized monitoring protocols and further interdisciplinary research. In light of the One Health approach, understanding the microbial dimension of MP pollution is essential for managing risks to environmental and public health. Full article

Leakages in water distribution networks pose a formidable challenge, often leading to substantial water wastage and escalating operational costs. Traditional methods for leak detection often fall short, particularly when dealing with complex or subtle data patterns. To address this, a comprehensive comparison of fourteen machine learning algorithms was conducted, with evaluation based on key performance metrics such as multi-class classification metrics, micro and macro averages, accuracy, precision, recall, and F1-score. The data, collected from an experimental site under leak, major leak, and no-leak scenarios, was used to perform multi-class classification. The results highlight the superiority of models such as Random Forest, K-Nearest Neighbours, and Decision Tree in detecting leaks with high accuracy and robustness. Multiple models effectively captured the nuances in the data and accurately predicted the presence of a leak, burst, or no leak, thus automating leak detection and contributing to water conservation efforts. This research demonstrates the practical benefits of applying machine learning models in water distribution systems, offering scalable solutions for real-time leak detection. Furthermore, it emphasises the role of machine learning in modernising infrastructure management, reducing water losses, and promoting the sustainability of water resources, while laying the groundwork for future advancements in predictive maintenance and resilience of water infrastructure. Full article

This review examines water pressure regulation technologies in irrigation systems tailored for hilly and mountainous terrains. In such areas, effective water management is crucial due to the terrain’s complexity and variability, which can greatly affect water distribution and resource efficiency. This text analyzes various types of pressure-regulating devices, including direct-acting and pilot-operated regulators, delving into their working principles, performance characteristics, and practical advantages and disadvantages. This summary also addresses the current research trends in these technologies, focusing on design optimization and performance enhancements. By summarizing existing studies and highlighting areas for future research, this review aims to provide a solid foundation for technological advancements in agricultural irrigation systems suited to challenging landscapes. Full article

Sediment accumulation in irrigation channels poses a significant challenge to water resource management, impacting hydraulic efficiency and agricultural sustainability. This study introduces an innovative multidisciplinary framework that integrates advanced image analysis (FIJI/ImageJ 1.54p), statistical validation (RStudio), and vector field modeling with a novel Sinusoidal Morphodynamic Bedload Transport Equation (SMBTE) to predict sediment deposition patterns with high precision. Conducted along the Malacatos River in La Tebaida Linear Park, Loja, Ecuador, the research captured a natural sediment transport event under controlled flow conditions, transitioning from pressurized pipe flow to free-surface flow. Observed sediment deposition reduced the hydraulic cross-section by approximately 5 cm, notably altering flow dynamics and water distribution. The final SMBTE model (Model 8) demonstrated exceptional predictive accuracy, achieving RMSE: 0.0108, R²: 0.8689, NSE: 0.8689, MAE: 0.0093, and a correlation coefficient exceeding 0.93. Complementary analyses, including heatmaps, histograms, and vector fields, revealed spatial heterogeneity, local gradients, and oscillatory trends in sediment distribution. These tools identified high-concentration sediment zones and quantified variability, providing actionable insights for optimizing canal design, maintenance schedules, and sediment control strategies. By leveraging open-source software and real-world validation, this methodology offers a scalable, replicable framework applicable to diverse water conveyance systems. The study advances understanding of sediment dynamics under subcritical (Fr ≈ 0.07) and turbulent flow conditions (Re ≈ 41,000), contributing to improved irrigation efficiency, system resilience, and sustainable water management. This research establishes a robust foundation for future advancements in sediment transport modeling and hydrological engineering, addressing critical challenges in agricultural water systems. Full article

Nitrifying bacteria, including ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB), are players in the nitrogen cycle but pose serious health risks when colonizing drinking water distribution networks (DWDNs). While the global impact of these bacteria is increasingly recognized, a significant research gap remains concerning their effects in tropical regions, particularly in developing countries. This study aims to bridge that gap by systematically reviewing the existing literature on nitrifying bacteria in DWDNs, their behavior in biofilms, and associated public health risks, particularly in systems reliant on surface water sources in tropical climates. Using the PRISMA guidelines for systematic reviews, 51 relevant studies were selected based on content validity and relevance to the research objective. The findings highlight the critical role of nitrifying bacteria in the formation of nitrogenous disinfection by-products (N-DBPs) and highlight specific challenges faced by developing countries, including insufficient monitoring and low public awareness regarding safe water storage practices. Additionally, this review identifies key surrogate indicators, such as ammonia, nitrite, and nitrate concentrations, that influence the formation of DBPs. Although health risks from nitrifying bacteria are reported in comparable studies, there is a lack of epidemiological data from tropical regions. This underscores the urgent need for localized research, systematic monitoring, and targeted interventions to mitigate the risks associated with nitrifying bacteria in DWDNs. Addressing these challenges is essential for enhancing water safety and supporting sustainable water management in tropical developing countries. Full article

Continuous damming in karst rivers fragmented the longitudinal structure of river systems, disrupting plankton habitats, limiting dispersal, and reducing biodiversity. This study examined variations in zooplankton functional diversity in a dammed river system during dry and wet seasons. Sampling across both seasons yielded 44 samples, with 64 zooplankton taxa categorized into seven functional groups based on their traits. Functional diversity indices were calculated. Results revealed significant differences in nutrient concentrations between upstream and downstream sections, particularly during the dry season (R² = 0.11, p < 0.01). Zooplankton functional diversity decreased from upstream to downstream, with more pronounced differences in the dry season (R² = 0.94, p < 0.05), driven by reduced dispersal stochasticity (β_BC close to −1). Continuous damming primarily affected smaller zooplankton, such as rotifers, while dissolved oxygen, water temperature, and pH influenced distribution patterns related to habitat depth, breeding season, life span, and reproduction. These findings underscored the impact of damming on zooplankton functional diversity and informed dam management strategies for biodiversity conservation. Full article

Urban river ecosystems are increasingly threatened by anthropogenic activities, with wastewater discharge being a significant contributor. The complex nature and diverse sources of wastewater pose challenges in assessing its impact on water quality and ecological health. This study investigated the distribution, toxicity, and ecological effects of organic pollutants in an urban river system during the dry season. A comprehensive analysis was conducted of 16 phthalate esters (PAEs), 16 polycyclic aromatic hydrocarbons (PAHs), and 8 antibiotics, with a focus on several key pollutants. The results revealed distinct pollutant profiles: Dibutyl phthalate (DBP), Dimethyl phthalate (DEHP), and Diisobutyl phthalate (DIBP) were the predominant PAEs, while Chrysene was the most abundant PAH. Among antibiotics, Oxytetracycline and Norfloxacin were the dominant compounds. Wastewater treatment plant (WWTP) effluents significantly altered the composition of organic pollutants in receiving waters. Although dilution reduced the concentrations of some pollutants, certain organic compounds were detected for the first time downstream of the WWTP, and some specific compounds exhibited increased concentrations. Toxicity prediction using the Concentration Addition (CA) model identified DBP as the primary contributor to overall toxicity, accounting for the highest toxic load among all detected pollutants. Furthermore, WWTP effluents induced significant shifts in microbial community structure downstream, with incomplete recovery to upstream conditions. Integrated analysis of 16S rRNA gene sequencing, water quality assessment, and toxicity prediction elucidated the multifaceted impacts of pollution sources on aquatic ecosystems. This study provides critical insights into the composition, spatial distribution, and toxicity characteristics of organic pollutants in urban rivers, as well as their effects on bacterial community structure. The findings offer a scientific foundation for urban river water quality management and ecological protection strategies. Full article

Global changes and society’s development necessitate the improvement of water use and irrigation water saving, which require a set of water management measures to best deal with the necessary changes. This study considers the framework of the change process for water management in the Hetao Irrigation District (HID) of the Yellow River Basin. This paper presents the main measures that have been applied to ensure the sustainability and modernization of Hetao, mitigating water scarcity while maintaining land productivity and environmental value. Several components of modernization projects that have already been implemented are characterized, such as the off-farm canal distribution system, the on-farm surface irrigation, innovative crop and soil management techniques, drainage, and salinity control, including the management of autumn irrigation and advances of drip irrigation at the sector and farm levels. This characterization includes technologies, farmer training, labor needs, energy consumption, water savings, and economic aspects, based on data observed and reported in official reports. Therefore, this study integrates knowledge and analyzes the most limiting aspects in some case studies, evaluating the effectiveness of the solutions used. Full article