Applied Water Science最新文献

Water security, the fundamental guarantee for socioeconomic development, is the basic prerequisite for ensuring humans have access to sufficient and safe water resources. In this study, a comprehensive water security evaluation system was constructed from four dimensions: water quantity, water quality, water pollution, and flood disasters. A system-dynamics simulation model for water security was developed. By coupling the Shared Socioeconomic Pathways and Representative Concentration Pathways, predictions were made about the future development trends of China’s water security. The results indicate that China’s water security situation shows significant volatility. The total water supply peaked in 2025. The compliance rate of drinking-water sources reached 98% and stabilized in 2034. The chemical oxygen demand (COD) emissions of industrial and domestic sewage peaked in 2020 and 2034 respectively, and the direct economic losses caused by flood disasters increased cyclically. In future scenario simulations, the water security situation will reach its optimal state under the SSP1-RCP2.6 scenario, while it will be most severe under the SSP5-RCP8.5 scenario. By 2052, the proportion of provinces with an “excellent” level in the water-quantity and water-quality subsystems will reach 52% and 77.4% respectively, mostly concentrated in economically developed regions. The balance between water supply and demand is the primary factor driving changes in water security. These results highlight the necessity of researching the stress factors and stress mechanisms influencing water security in the context of climate change.

Smart irrigation systems utilising the Internet of Things (IoT) technology and sensory systems have emerged as a revolutionary approach to modernising agriculture and addressing sustainability challenges. The worldwide agricultural sector continues to struggle with two major problems, which include water resource depletion and high operational expenses because of outdated irrigation systems. The research investigates the immediate requirement for budget-friendly precision agriculture solutions that can serve small to medium farmers operating in limited resource areas. The paper describes the development of an affordable IoT-based smart irrigation system that underwent experimental testing. The system uses an ESP32 microcontroller as its core component while incorporating a capacitive soil moisture sensor for precise measurements, a DHT11 sensor for environmental data collection, and the Blynk IoT platform for live monitoring and distant system operation. The system begins irrigation when soil moisture reaches 20% and stops irrigation when moisture levels reach 80%. The system achieved operational success through field tests, which showed it used 25–35% less water than fixed-schedule irrigation systems while reducing operational expenses. The research proves that this affordable design can be duplicated and shows both technical and financial viability, which makes it an effective solution for sustainable farming practices.

Surface irrigation, despite its relatively simple design and lower costs compared to pressurized systems, still faces challenges such as water loss and non-uniform moisture distribution. The SCS method, as one of the widely used surface irrigation design methods, has not yet benefited from the necessary improvements and adaptations to improve performance. In this study, a simulation–optimization model for the optimal design of basin irrigation was developed to improve hydraulic performance using the MATLAB programming software. In the simulation part of the model, the SCS method was modified, and in the optimization part, the Grey Wolf Optimizer (GWO) meta-heuristic algorithm was employed. By implementing changes in the inputs and outputs of the SCS method, and enhancing the calculations, more accurate and more coordinated optimization with real conditions was enabled. These modifications included converting the basin length into a decision variable during the optimization process, and improving calculations related to advance time, infiltrated water depth, cutoff, depletion and recession times, as well as infiltrated water volume, and deep percolation at different points of the basin. The results of optimization after simulation of basin irrigation design in the experimental field demonstrated positive effects of optimization on performance indicators such as Application Efficiency (Ea), Distribution Uniformity (DU), and Requirement Efficiency (Er). In the initial design, despite considerable deep percolation and relatively long advance time, the Ea was 61% and DU was 84%. However, after optimization, changes in the basin length and discharge variables led to reductions in advance time and cutoff time, resulting in a 27% decrease in the Deep Percolation Ratio (DPR), and a 13% increase in DU. Additionally, with reductions in water consumption volume and deep percolation, Ea improved by 27%. The objective function value decreased (improved) from 0.93 in the initial design to 0.22 in the optimized design, indicating a significant improvement in irrigation system efficiency. These changes were mainly achieved by reducing the basin length, and increasing discharge to reduce advance time, which proved to be the primary effective strategy in the optimized condition. The results also indicated that variations in basin length had a greater impact than discharge values in achieving the optimal state. Overall, the developed model was capable of providing a framework for the optimal design of basin irrigation.

The economic and environmental commodity of water quality has a substantial impact on the welfare of the public and the viability of ecosystem of a country. This research aims to improve the accuracy of water quality index (WQI) prediction by using machine learning techniques based on Malaysian standard. The study is carried out using a historical dataset that includes 11,065 samples from different places by the Malaysian Department of Environment (DOE). The data contains several important water quality parameters, including pH, dissolved oxygen, total suspended solids, biological oxygen demand, chemical oxygen demand, and ammoniacal nitrogen concentration. We develop a novel model, time series regression forest (TSRF) that is based on regression and random forest theory to enhance the WQI prediction. TSRF is compared with other machine learning models: decision trees (DT), ridge regression (RR), artificial neural networks (ANN), extra tree regressor (ETR), random forest (RF), autoregressive integrated moving average (ARIMA), DT-ARIMA and RF-ARIMA models. TSRF achieves the lowest mean square error (MSE) of 1.178 and the highest R² of 0.968, surpassing DT–ARIMA (MSE = 2.526, R² = 0.932) and RF–ARIMA (MSE = 2.907, R² = 0.922). The comparison with deep learning approaches shows that long short term memory (LSTM) achieves MSE = 9.649 and R² = 0.879, followed closely by convolutional neural network (CNN)-LSTM (MSE = 13.224, R² = 0.834). In contrast, gated recurrent unit (GRU) (R² = 0.781), Transformer (R² = 0.781), and Wavelet-ARIMA (R² = 0.676) exhibit higher errors and weaker correlations, highlighting the relative advantage of temporal deep learning models for dynamic water quality forecasting. These results indicate that TSRF can improve the reliability and efficiency of WQI prediction, enabling policymakers and environmental managers to make informed, data-driven decisions and implement targeted pollution control strategies. Thus, this study can assist us to have better water quality management strategies by increasing the reliability and efficiency of WQI prediction. Water quality assessment methods developed in this research can help policymakers and environmental stewards to make informed decisions and implement targeted pollution control strategies. Advancing machine learning applications in environmental science strengthens current practices in water quality management and enhances decision-making capabilities.

Small inland wetlands, despite their pivotal roles in supporting aquatic biodiversity and local livelihoods, remain understudied and vulnerable to rapid degradation. This study investigates small wetlands in India’s Ganjam District, hypothesizing that urban pressures accelerate degradation, with light pollution and population density serving as effective water quality predictors. To test these assumptions, we measured 25 physicochemical variables and focused on 11 key parameters for the CCME water quality index (WQI). The scores ranged from 43 to 71, with 63% of sites rated ‘marginal’, reflecting elevated ammonium (> 0.5 mg L⁻¹), turbidity (> 10 NTU), total Fe (> 2 mg L⁻¹), and organic loads in most sites. We supplemented these data with night‐light intensity and population density rasters. Two principal components jointly explained approximately 50% of the total data variability. The first component and hierarchical clustering revealed a dominant brackish axis driven by salinity, hardness, TDS; and a turbidity–nutrient axis capturing total phosphorus, suspended solids, and turbidity. Meanwhile, light pollution and population density showed strong negative correlations with WQI, highlighting their power to track anthropogenic influence beyond conventional chemical variables. This study reiterates that fact that surveillance of smaller ecosystems can be a more representative and realistic reflection of overall habitat quality in any region, as compared with the perceived notion of focusing on larger ecosystems in sync with the SLOSS theory. The insights highlight the need for integrated management addressing both nutrient regulation and socioeconomic pressures to protect fragile waterbodies.

This study investigated the effect of powdered Shale and carbon nanotubes (FMWCNT) with polar functional groups OH and COOH on the rheological properties of lightweight drilling fluid weighing 63 pcf. In the first step, a fluid was used as a base, and six fluids containing nanotubes (three samples with OH functional groups and three samples with COOH functional groups) were prepared at ambient BHR (77 °F) and mud circulation at simulated bottom hole temperature (250 °F) at concentrations of 0.01, 0.05, and 0.1% (w/w). After that, the rheological properties and filtration values (FL) were measured. The results show that the highest effect was observed at the concentration of 0.05% for the COOH functional group, which increased the rheological properties in both BHR and AHR conditions and decreased the FL values. In addition, the based mud was significantly improved by formulating it with 20 g of powdered Shale and 0.05% nanotubes with COOH functional groups. Compared to the based mud, the apparent viscosity and filtration values (AHR condition) were 37.5% and 67.8% improved, respectively.

Surface water resources in the Gorganroud watershed are facing significant reliability challenges due to human activities and climate change. This research aims to assess the resilience, reliability, and vulnerability of water resources across 16 sub-watersheds, focusing on minimum, maximum and environmental flows as well as water quality. The findings indicate that the health of the minimum flow over periods of 7, 30, and 90 days is rated at 0.16, 0.32, and 0.45, respectively, reflecting unhealthy to moderate conditions at the watershed level. Additionally, the maximum flow health score of 0.34 highlights the necessity for effective flood management. Water quality assessments for drinking and agricultural purposes were estimated at 0.52 and 0.57, respectively; however, downstream agricultural sub-watersheds with intensive activities, such as Hajighoshan, Dasht, Gonbad, Ghazaghli, and Gorgan Dam, are facing critical conditions. The overall health of the environmental flow in the watershed is rated at 0.47, with the middle and lower sub-watersheds, including Dasht and Gorgan Dam, exhibiting poor conditions, while mountainous areas, such as Till Abad, demonstrate better performance. These results underscore the urgent need for integrated water resource management, vegetation restoration, and the control of agricultural pollution to enhance watershed health.

Groundwater is an important natural resource currently used for drinking, irrigation and industrial purposes, being deteriorated by natural and anthropogenic sources of pollution. The main aim of this is to assess groundwater hydrochemistry, salinity origins, recharge sources, and water quality in the Kashmir basin using geochemical modeling and multivariate statistics. The observed range of total dissolved solids (140 ≤ TDS ≤ 2226 mg/L) and chloride (10.3 ≤ Cl⁻ ≤ 408.5 mg/L) in groundwater specify a mixed source of salinity, stemming from both natural geological formations and human activities. The average concentrations of cations and anions in groundwater are in order of Na⁺  > Ca²⁺  > Mg²⁺  > K⁺ and HCO₃⁻ > SO₄²⁻ > Cl⁻ > NO₃⁻, respectively. The groundwater is categorized as fresh to very hard water in study area. Hydrochemistry, based on major ions were predominantly; Ca-HCO3 (40%), mixed Ca–Mg–Cl-SO₄ (30%), Na-Cl (10%), Na–K-HCO3 (15%) and Ca–Cl (5%) in study area. Stable isotopic and hydrochemical signatures clearly explained recharge of groundwater from rain and surface water, and origin of salinity in aquifer by dissolution of salts and rock-water interaction processes. Lower values of δ¹³C_DIC, in groundwater is interpreted as evidence that biogenic CO₂, produced by the microbial oxidation of organic matter within the aquifer, is a significant source contributing to the dissolved inorganic carbon pool in groundwater. Geochemical modeling of groundwater indicates the water is under-saturated with respect to gypsum, anhydride, halite, and sylvite minerals, while being at equilibrium or slightly over-saturated with respect to carbonate minerals, suggesting significant water–rock interactions. Multivariate statistical analyses show natural weathering processes as well as anthropogenic sources of contamination in groundwater. Overall, computed water quality indices (WQIs) demonstrated that 12.5% and 85% water samples come under “excellent “ and “good “ quality, however 2.5% sample exhibited poor category. Irrigation water quality indices (IWQIs) appeared that groundwater in the study area is good quality to support the irrigation needs of crops, opening up opportunities for diverse and potentially more profitable agricultural activities.

Groundwater has been the main source of irrigation in Morocco for many decades. However, irrigated agriculture is now constrained by groundwater depletion, severe soil salinity and alkalinity, among other factors. Irrigation practices with poor quality groundwater can limit crop choices, reduce agricultural yields and lead to long-term deterioration of soil quality and fertility. This study aims to identify the hydrogeochemical processes controlling groundwater composition in the Triffa plain study area and evaluates the effects of depth (shallow vs. deep) on its suitability for irrigation, in relation to dominant soil types, by adopting a comprehensive approach, combining the Irrigation Water Quality Index (IWQI), multiple traditional indices (e.g., SAR, SARadj, %Na, PS, PI, KR, SSP, MAR, RSBC, TH), USSL/Wilcox/Doneen diagrams, statistical analyses and GIS technique. The hydrogeochemical analysis reveals that groundwater in the Triffa plain is dominated by Cl⁻ and Na⁺ ions, with three main hydrochemical facies identified: Na⁺–Cl⁻, Ca²⁺–Mg²⁺–Cl⁻–SO₄²⁻, and Ca²⁺–Mg²⁺–Cl⁻ mixed type. The salinity and ionic composition of groundwater in the Triffa plain are primarily influenced by evaporite dissolution minerals, carbonate weathering and ion exchange processes, with no evidence of seawater intrusion, as demonstrated by the Cl⁻/Br⁻ ratio. Most groundwater samples were found very hard (TH > 300 mg/L) and display high ECw and TDS values, indicating poor suitability for irrigation, except dam water. IWQI results show that 88.88% of shallow and deep borehole groundwater falls within high to severe restriction use categories, which is confirmed by traditional indices and diagrams (USSL and Wilcox). Only 11.12% of samples were classified in the low to moderate restriction categories, especially those from dam water. Spatial analysis reveals that water quality deteriorates from the southern/southeastern zones to the central and northern regions of the plain, with the highest restrictions found in the northern regions, which present elevated values across different indices (e.g., SAR, %Na, KR), ECw, TDS, Na⁺, and Mg²⁺. To address salinity and sodicity risks, especially in Mollisols and Ultisols, this study advises soil-specific management, especially under high to severe restriction zones, with application of periodic leaching, very frequent irrigation, gypsum amendments, low-volume irrigation, and the use of polyacrylamides (PAMs) to mitigate salinity effects and preserve soil structure. Also, this study offers a promising and complete approach for assessing water irrigation quality in semi-arid areas for water management, soil quality, and sustainable agriculture.

Water quality is a crucial index of human health and environmental sustainability. The present study aimed to apply deterministic, probabilistic, and ML techniques, such as RF, DT, KNN, XGB, SVR, and GB, to classify the water quality in the southern region of Iran. The levels of TDS and alkalinity exhibited the greatest deviation from the standards set by the EPA, WHO, and BIS. The WQI findings revealed that 88.30% of the data were classified as excellent or good quality when employing the deterministic method. Conversely, 97.77% fell within these categories when the Monte Carlo simulation approach was used. The models were meticulously assessed using a set of statistical metrics, including R², MAE, RMSE, MSE, and PREI. The results show that the RF and XGB were highly effective in predicting WQI. The factors influencing the WQI, identified by RF and XGB methodologies and based on MLP, were TDS and SO₄²⁻ within the study area. According to the Piper diagram, the predominant groundwater type in the study region was HCO₃⁻–Na⁺, influenced by seawater intrusion, geological properties, and human activities. The deterministic method showed that the HI values exceeded the threshold of 1 in 51%, 2.13%, and 2.13% of the samples for children, teenagers, and adults, respectively. In contrast, the Monte Carlo simulation approach indicated that the HI values exceeding 1 were 34.8% for children, 2.9% for teenagers, and 0.4% for adults. Moreover, the HI was significantly affected by fluoride concentration, ingestion rate, and their interaction. The study's findings emphasized sustainable water management in this area.