Monitoring Of Water Resources Research Articles

Surface water quality prediction in the lower Thoubal river watershed, India: A hyper-tuned machine learning approach and DNN-based sensitivity analysis

The accurate and efficient monitoring, assessment, and prediction of water resources is crucial for the sustenance of life and health of the environment. Traditional methods for assessing water quality can be laborious and time-consuming, but the use of machine learning algorithms can improve the speed and precision of these predictions. The study aimed to evaluate the quality of surface water in the lower Thoubal river using a comprehensive approach that analyzed 12 physico-chemical properties of the water samples collected from 16 different sites. Four machine learning algorithms namely, deep neural network (DNN), gradient boost model (GBM), generalized linear model (GLM), and random forest (RF) were applied and compared for the prediction efficiency of a water quality index (WQI). Sensitivity and uncertainty analysis of the parameters were carried out using a DNN-based model. The study incorporated model evaluation using learning curve along with five key performance assessors. The findings revealed marked variation in the WQI in the watershed. Of the samples tested, about 70 % were found to have good water quality, 18 % had excellent water quality, 11 % had poor water quality, 1 % had very poor water quality, and less than 1 % were deemed unsuitable. Turbidity, BOD, and COD were found to be the most influential parameter towards WQI prediction. The effectiveness of the predictive models ranked as GLM>DNN>RF>GBM, GLM>GBM>DNN>RF, and GLM>DNN>RF>GBM; based on testing, training and 5-fold cross-validation. The GLM model consistently showed superior performance with the lowest RMSEs of 0.14783 in training, 0.15936 in validation, and 0.26115 in 5-fold cross-validation. Conversely, during the training, RF displayed the worst RMSEs of 5.38354, 4.79754 by GBM in validation, and 7.04887 by GBM in 5-fold cross-validation. The analysis using the Taylor diagram and learning curve further supported that GLM was the most effective model for predicting surface water quality. Biological oxygen demand, chemical oxygen demand, electrical conductivity, turbidity and total hardness were found to be the key parameters influencing the water quality. Unregulated and illegal sand mining from the riverbed, garbage dumping into the water bodies, runoffs from agricultural fields have significantly deteriorated the water quality in the study area. The scalable approach of the study and its results can benefit the local water managers and water research community.

Performance of water indices for large-scale water resources monitoring using Sentinel-2 data in Ethiopia

Evaluating the performance of water indices and water-related ecosystems is crucial for Ethiopia. This is due to limited information on the availability and distribution of water resources at the country scale, despite its critical role in sustainable water management, biodiversity conservation, and ecosystem resilience. The objective of this study is to evaluate the performance of seven water indices and select the best-performing indices for detecting surface water at country scale. Sentinel-2 data from December 1, 2021, to November 30, 2022, were used for the evaluation and processed using the Google Earth Engine. The indices were evaluated using qualitative visual inspection and quantitative accuracy indicators of overall accuracy, producer’s accuracy, and user’s accuracy. Results showed that the water index (WI) and automatic water extraction index with shadow (AWEIsh) were the most accurate ones to extract surface water. For the latter, WI and AWEIsh obtained an overall accuracy of 96% and 95%, respectively. Both indices had approximately the same spatial coverage of surface water with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia. The results provide a valuable insight into the extent of surface water bodies, which is essential for water resource planners and decision-makers. Such data can also play a role in monitoring the country’s reservoirs, which are important for the country’s energy and economic development. These results suggest that by applying the best-performing indices, better monitoring and management of water resources would be possible to achieve the Sustainable Development Goal 6 at the regional level.

Towards interpretability lightweight semantic segmentation model for waterbody extraction in large-scale high resolution remote sensing images

ABSTRACT In recent years, high resolution (HR) remote sensing images have brought significant changes to land cover monitoring. Monitoring water resources, a vital and scarce commodity for human survival, is a crucial aspect of land cover monitoring and forms the foundation for water resource allocation in regions facing shortages. Hence, a universal method is necessary to address this problem and handle large-scale water resources monitoring. We explore the network structure via improving the interpretability to reduce the network layers and propose a lightweight pixel-wise semantic segmentation model, which achieves waterbody extraction from GF-1 remote sensing images with high accuracy and high speed. The proposed model structure addresses both semantic segmentation for large and small waterbodies, extracting fine-grained edge and contour information from high-resolution feature maps. Additionally, it efficiently extracts multi-scale high-level semantic information using residual convolutional blocks and dilated convolutional blocks. The multi-scale feature maps are fused, and binary classification is predicted through Support Vector Machines (SVM). Furthermore, the paper introduces a model training method with an adaptive learning rate, reducing the overall training time. To validate the model’s performance, remote sensing images from GF-1 are utilized to construct a dataset. Experimental results, compared with five models, demonstrate that the proposed method achieves the highest accuracy and least training time.

Remote sensing and monitoring of water resources: A comparative study of different indices and thresholding methods

Water resources are essential for the ecological system and the development of civilization. Water is imperative factor for health preservation and sustaining various human activities, including industrial production, agriculture, and daily life. Remote sensing provides a cost-effective and practical means to detect and monitor water bodies, offers valuable insights into the impact of climatic events on water structures, especially in coastal lake regions. The research primarily utilizes Landsat-9 OLI-2 satellite images to evaluate the effectiveness of various water indices (WRI, NWI, MNDWI, NDWI) in combination with global automatic thresholding methods (K-Means, Zhenzhou's, Adaptive, Intermodes, Prewitt and Mendelsohn's Minimum, Maximum Entropy, Median, Concavity, Percentile, Intermeans, Kittler and Illingworth's Minimum Error, Tsai's Moments, Otsu's, Huang's fuzzy, Triangle, Mean, IsoData, Li’s). The study was carried out on Lake Nazik, Lake Iznik, and Lake Beyşehir, which have unique geographical characteristics, and examined the adaptability and robustness of the selected indices and thresholding methods. MNDWI consistently stands out as a robust index for water extraction, delivering accurate results across different thresholding methods in regions all three lakes. As a result of quite extensive analysis, it is obtained that MNDWI and NDWI are reliable choices for water feature extraction in various lake environments, but the specific index should consider the thresholding method and unique lake characteristics. The Minimum thresholding method stands out as the most effective thresholding technique, demonstrating impressive results across different lakes. Specifically, it achieved an average Peak Signal-to-Noise Ratio (PSNR) of 78.97 and Structural Similarity Index (SSIM) of 99.37 for Lake Nazik, 74.08 PSNR and 98.34 SSIM for Lake Iznik, and 63.96 PSNR and 93.61 SSIM for Lake Beyşehir.

Chlorophyll-a unveiled: unlocking reservoir insights through remote sensing in a subtropical reservoir

Effective water resources management and monitoring are essential amid increasing challenges posed by population growth, industrialization, urbanization, and climate change. Earth observation techniques offer promising opportunities to enhance water resources management and support informed decision-making. This study utilizes Landsat-8 OLI and Sentinel-2 MSI satellite data to estimate chlorophyl-a (chl-a) concentrations in the Nandoni reservoir, Thohoyandou, South Africa. The study estimated chl-a concentrations using random forest models with spectral bands only, spectral indices only (blue difference absorption (BDA), fluorescence line height in the violet region (FLH_violet), and normalized difference chlorophyll index (NDCI)), and combined spectral bands and spectral indices. The results showed that the models using spectral bands from both Landsat-8 OLI and Sentinel-2 MSI performed comparably. The model using Sentinel-2 MSI had a higher accuracy of estimating chl-a when spectral bands alone were used. Sentinel-2 MSI’s additional red-edge spectral bands provided a notable advantage in capturing subtle variations in chl-a concentrations. Lastly, the –chl-a concentration was higher at the edges of the Nandoni reservoir and closer to the reservoir wall. The findings of this study are crucial for improving the management of water reservoirs, enabling proactive decision-making, and supporting sustainable water resource management practices. Ultimately, this research contributes to the broader understanding of the application of earth observation techniques for water resources management, providing valuable information for policymakers and water authorities.

Geospatial Technology for Sustainable Agricultural Water Management in India—A Systematic Review

Effective management of water resources is crucial for sustainable development in any region. When considering computer-aided analysis for resource management, geospatial technology, i.e., the use of remote sensing (RS) combined with Geographic Information Systems (GIS) proves to be highly valuable. Geospatial technology is more cost-effective and requires less labor compared to ground-based surveys, making it highly suitable for a wide range of agricultural applications. Effectively utilizing the timely, accurate, and objective data provided by RS technologies presents a crucial challenge in the field of water resource management. Satellite-based RS measurements offer consistent information on agricultural and hydrological conditions across extensive land areas. In this study, we carried out a detailed analysis focused on addressing agricultural water management issues in India through the application of RS and GIS technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we systematically reviewed published research articles, providing a comprehensive and detailed analysis. This study aims to explore the use of RS and GIS technologies in crucial agricultural water management practices with the goal of enhancing their effectiveness and efficiency. This study primarily examines the current use of geospatial technology in Indian agricultural water management and sustainability. We revealed that considerable research has primarily used multispectral Landsat series data. Cutting-edge technologies like Sentinel, Unmanned Aerial Vehicles (UAVs), and hyperspectral technology have not been fully investigated for the assessment and monitoring of water resources. Integrating RS and GIS allows for consistent agricultural monitoring, offering valuable recommendations for effective management.

Groundwaters in the Auvergne-Rhône-Alpes Region, France: Grouping Homogeneous Groundwater Bodies for Optimized Monitoring and Protection

The number and diversity of groundwater bodies (GWBs) in large French administrative regions pose challenges to their monitoring and protection by regional health agencies. To overcome this obstacle, we propose, for the Auvergne-Rhône-Alpes region (about 70,000 km2), a grouping of GWBs into homogeneous groups based on the sources of variability within a large dataset of groundwater physico-chemical and bacteriological characteristics (8078 observations and 13 parameters). This grouping involved a dimensional reduction in the data hyperspace by principal component analysis (PCA) and a clustering based on the mean values of each GWB on the factorial axes. The information lost when clustering from the sample point scale to the GWB scale and then to that of the GWB group was quantified by analysis of variance and showed that grouping GWBs is accompanied by a small loss of information. A discriminant analysis confirmed the high spatial and temporal variability within the dataset, as well as the effectiveness of the proposed method for establishing homogeneous sets. Some roadmaps for more targeted monitoring of water resources were briefly proposed.

Fabrication and application of functionalized carbon nanotubes for highly sensitive and selective sensing of enrofloxacin

AbstractThis paper introduces an innovative electrochemical sensor that detects enrofloxacin (ENR) utilizing a glassy carbon electrode (GCE) modified with reduced carbon nanotubes (rCNT). The fabrication process involved a simple drop‐coating technique using water‐soluble oxidized CNTs (oCNT), followed by electrochemical reduction to obtain the rCNT/GCE electrode. The electrochemical properties of the modified electrodes, including GCE, CNT/GCE, oCNT/GCE, and rCNT/GCE, were evaluated using cyclic voltammetry and electrochemical impedance spectroscopy. The results demonstrated significantly improved electrochemical surface area and faster electron transfer rate for the rCNT/GCE electrode compared to the other configurations. Consequently, the rCNT/GCE electrode exhibited the highest electrochemical signal for ENR detection. Under optimized conditions, the rCNT/GCE‐based sensor displayed excellent performance for ENR detection. It achieved a low detection limit of 0.002 µm and demonstrated linearity within the ranges of 0.005–0.05 µm and 0.05–1.5 µm. Furthermore, the proposed sensor exhibited good selectivity, as well as excellent repeatability and reproducibility. To assess the sensor's practical utility, a quantitative analysis of ENR in shrimp meat samples was conducted. The results demonstrated the high sensitivity and accuracy of the proposed sensor in quantifying ENR, highlighting its potential as a reliable tool for environmental monitoring of water resources.

Global hydrological reanalyses: The value of river discharge information for world‐wide downstream applications – The example of the Global Flood Awareness System GloFAS

AbstractGlobal hydrological reanalyses are modelled datasets providing information on river discharge evolution everywhere in the world. With multi‐decadal daily timeseries, they provide long‐term context to identify extreme hydrological events such as floods and droughts. By covering the majority of the world's land masses, they can fill the many gaps in river discharge in‐situ observational data, especially in the global South. These gaps impede knowledge of both hydrological status and future evolution and hamper the development of reliable early warning systems for hydrological‐related disaster reduction. River discharge is a natural integrator of the water cycle over land. Global hydrological reanalysis datasets offer an understanding of its spatio‐temporal variability and are therefore critical for addressing the water–energy–food–environment nexus. This paper describes how global hydrological reanalyses can fill the lack of ground measurements by using earth system or hydrological models to provide river discharge time series. Following an inventory of alternative sources of river discharge datasets, reviewing their advantages and limitations, the paper introduces the Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) modelling chain and its reanalysis dataset as an example of a global hydrological reanalysis dataset. It then reviews examples of downstream applications for global hydrological reanalyses, including monitoring of land water resources and ocean dynamics, understanding large‐scale hydrological extreme fluctuations, early warning systems, earth system model diagnostics and the calibration and training of models, with examples from three Copernicus Services (Emergency Management, Marine and Climate Change).

Change in water quality in an Amazonian microbasin: ecological and human health implications.

The decline in the quality of water resources in the Amazon is very rapid in cities suffering from unplanned urban growth. The region has two defined seasons, winter (wet) and summer (dry), which directly affect the behavior of contaminants in aquatic ecosystems. The aim of this study was to assess the ecological and human health risks associated with the use of the watershed. In addition, an ecological index was proposed: the Quality Index for Aquatic Life, for the risk of contaminants to aquatic life. Sampling was carried out at six points in the Juá watershed. Physicochemical parameters, major anions, metals and total phosphorus were analyzed at both stations between 2020 and 2021. The highest concentrations of contaminants were found in the rainy season, due to the washing away of the banks. In this sense, Cl presented a concentration more than 307 times higher than that permitted by Brazilian legislation (wet). The ecological index showed that the watershed has a high risk of metals such as Cr III and Cr VI for the biota. The human health risk analysis showed a low risk; however, the lack of basic sanitation in the city indicates that monitoring of urban water resources is necessary.

Research on the use of sensor networks in underwater technologies

This study focuses on the application of sensor networks in the field of underwater technologies, opening new possibilities for monitoring and researching the marine environment. Main attention is given to the development and implementation of sensor systems capable of withstanding high pressure and corrosion in the underwater environment, as well as providing accurate and reliable data.
 The work covers the theoretical aspects of sensor networks, including their design, functional capabilities, and challenges associated with their use in the underwater environment. Particular attention is paid to data transmission methods and energy conservation in conditions of limited access to energy.
 The next component of the work is the analysis of the practical aspects of implementing sensor networks, particularly considering their application for monitoring water resources, studying marine fauna and flora, and in underwater archaeological research. The work emphasizes the importance of these technologies for collecting data about the seabed, its relief, and ecosystem.
 This scientific work considers the prospects for the development of sensor networks in underwater research, including integration with other technologies, such as autonomous underwater vehicles, and the development of new materials and technologies to enhance the efficiency and durability of sensor systems.

Development of an integrated approach to the analysis and forecast of hydrographic and bathymetric data of water bodies and tailings ponds

There is a need for an effective monitoring solution for water quality control in tailings dumps and adjacent water bodies in order to prevent environmental pollution. This article highlights the importance of water quality monitoring and surveillance to prevent pollution. It is proposed to develop a mobile robotic complex equipped with sensors for monitoring water bodies and tailings, which is also capable of measuring underwater topographic data. The objects of study were a tailings pond and water bodies. The authors analyzed existing technical monitoring solutions, designed and developed a robotic complex, echolocation device, tested them on specific sites (the tailings dump of the Zhayrem Mining and Processing Plant and the Ishim River), conducted laboratory analysis of water samples, classified the results. Additionally, they obtained 2D and 3D maps of the bottom, and entered all collected data into a developed database and software. The developed complex demonstrated high accuracy of movement (an error of about 0.2 m on the x axis and 0.1 m on the y axis) and the ability to register environmental parameters such as temperature, humidity, PH. Data analysis for 2021–2023 showed a significant excess of recycled water discharged into the evaporator pond, which emphasizes the importance of monitoring and management of water resources. The research applies ARIMA models, neural networks to predict water body parameters. The results obtained indicate the high efficiency of the developed robotic complex and methods for analyzing data on water resources. These methods can be used in industry, scientific research and environmental projects to regularly monitor water quality and take measures to protect it

Forecasting health risks for the residents of Southern Russia through satellite and climate-based aridity indicators

BACKGROUND: The aridification of the Southern region of Russia will lead to a significant rise in the concentration of hazardous substances in groundwater over the long term. This phenomenon poses a serious threat to the environment and public health.
 AIM: To assess the potential of the multi-regression climate model in predicting the long-term health risks associated with the ingestion of toxic substances released from groundwater sources.
 METHODS: An assessment of non-carcinogenic health risks (HI) was conducted for the period 2017–2022, focusing on three groundwater basins in the Volgograd Trans-Volga region. The study involved the analysis of toxicant concentrations in 1149 water samples at the 95th percentile. NDMI and De Martonne Index (DMI) values were calculated based on modified data obtained through Land Surface Temperature (LST) satellite analysis. These indices were then employed as predictors in our models, with the HI serving as the dependent variable.
 RESULTS: A significant contribution of chloroform to the overall risk pattern for groundwater in the Volgograd Trans-Volga region was observed. The maximum values were recorded in the Nizhnevolzhskiy groundwater basin (HQchildren/chloroform=3.20, HQadults/chloroform=1.37) in 2017. The satellite aridity index NDMI makes the greatest contribution to the reliability of the predictive model of long-term health risk dynamics that shape the oral intake of pollutants from groundwater in the Volgograd Trans-Volga region. The lowest multiple regression value was noted for the health risk for adults (ry,x1,x2=–0.909, p=0.012) in the Severo-Prikaspiyskiy basin, the maximum was recorded in Ryn-Peskovsky basin for children (ry,x1,x2=–0.992, p=0.002). The DMI provides insignificant reliability in predicting long-term dynamics of non-carcinogenic health risks associated with toxicants circulating in arid ecosystems of the South of Russia. The greatest contribution of this predictor was observed for the health risk of children in the Ryn-Peskovsky basin (rx2/x1=–0.554, p=0.105).
 CONCLUSION: Our findings suggest a significant potential for integrating NDMI in monitoring the social and hygienic quality of underground water in arid zones of Southern Russia. The NDMI indicator has demonstrated high resolution and sensitivity to water quantity in steppe vegetation, reflecting its accuracy for arid topography. This integration holds promise for enhancing the monitoring and management of underground water resources in Southern Russia.

Remote Sensing Extraction of Lakes on the Tibetan Plateau Based on the Google Earth Engine and Deep Learning

Lakes are an important component of global water resources. In order to achieve accurate lake extractions on a large scale, this study takes the Tibetan Plateau as the study area and proposes an Automated Lake Extraction Workflow (ALEW) based on the Google Earth Engine (GEE) and deep learning in response to the problems of a low lake identification accuracy and low efficiency in complex situations. It involves pre-processing massive images and creating a database of examples of lake extraction on the Tibetan Plateau. A lightweight convolutional neural network named LiteConvNet is constructed that makes it possible to obtain spatial–spectral features for accurate extractions while using less computational resources. We execute model training and online predictions using the Google Cloud platform, which leads to the rapid extraction of lakes over the whole Tibetan Plateau. We assess LiteConvNet, along with thresholding, traditional machine learning, and various open-source classification products, through both visual interpretation and quantitative analysis. The results demonstrate that the LiteConvNet model may greatly enhance the precision of lake extraction in intricate settings, achieving an overall accuracy of 97.44%. The method presented in this paper demonstrates promising capabilities in extracting lake information on a large scale, offering practical benefits for the remote sensing monitoring and management of water resources in cloudy and climate-differentiated regions.

Mapping Water Bodies and Wetlands from Multispectral and SAR Data for the Cross-Border River Basins of the Polish–Ukrainian Border

Using remote sensing data to accurately record water surface changes over large areas is crucial in monitoring water resources. However, mapping water surfaces from remote sensing data has its advantages and disadvantages. This study presents a method for mapping water surfaces and wetlands based on Sentinel-1/-2 data over a study area of more than 26,000 km2 in three river basins, the Bug, Dniester, and San, located along the Polish–Ukrainian border. To achieve this goal, an image processing algorithm with additional options was developed (special filters, type classification, and post-classification), which minimized the shortcomings and increased the accuracy of the method. As a result, by using optical and radar data, it was possible to create maps of water bodies in the study area in the driest month of the year from 2018 to 2021. The results were evaluated numerically and graphically. The accuracy of the method was assessed using the Kappa coefficient. For optical data, the lowest value was 76.28% and the highest was 88.65%; for radar data, these values were 87.61% and 97.18%, respectively. When assessing accuracy, the highest values were achieved for overall accuracy (OA), with a maximum of 0.95 (for SAR) and 0.91 (for optical data). The highest values were in user accuracy (UA), with a maximum value of 1 for both SAR and optical data.

Gaussian mutation–orca predation algorithm–deep residual shrinkage network (DRSN)–temporal convolutional network (TCN)–random forest model: an advanced machine learning model for predicting monthly rainfall and filtering irrelevant data

Monitoring water resources requires accurate predictions of rainfall data. Our study introduces a novel deep learning model named the deep residual shrinkage network (DRSN)—temporal convolutional network (TCN) to remove redundant features and extract temporal features from rainfall data. The TCN model extracts temporal features, and the DRSN enhances the quality of the extracted features. Then, the DRSN–TCN is coupled with a random forest (RF) model to model rainfall data. Since the RF model may be unable to classify and predict complex patterns and data, our study develops the RF model to model outputs with high accuracy. Since the DRSN–TCN model uses advanced operators to extract temporal features and remove irrelevant features, it can improve the performance of the RF model for predicting rainfall. We use a new optimizer named the Gaussian mutation (GM)–orca predation algorithm (OPA) to set the DRSN–TCN–RF (DTR) parameters and determine the best input scenario. This paper introduces a new machine learning model for rainfall prediction, improves the accuracy of the original TCN, and develops a new optimization method for input selection. The models used the lagged rainfall data to predict monthly data. GM–OPA improved the accuracy of the orca predation algorithm (OPA) for feature selection. The GM–OPA reduced the root mean square error (RMSE) values of OPA and particle swarm optimization (PSO) by 1.4%–3.4% and 6.14–9.54%, respectively. The GM–OPA can simplify the modeling process because it can determine the most important input parameters. Moreover, the GM–OPA can automatically determine the optimal input scenario. The DTR reduced the testing mean absolute error values of the TCN–RAF, DRSN–TCN, TCN, and RAF models by 5.3%, 21%, 40%, and 46%, respectively. Our study indicates that the proposed model is a reliable model for rainfall prediction.

FlowSOM clustering – A novel pattern recognition approach for water research: Application to a hyper-arid coastal aquifer system

Monitoring and understanding of water resources have become essential in designing effective and sustainable management strategies to overcome the growing water quality challenges. In this context, the utilization of unsupervised learning techniques for evaluating environmental tracers has facilitated the exploration of sources and dynamics of groundwater systems through pattern recognition. However, conventional techniques may overlook spatial and temporal non-linearities present in water research data. This paper introduces the adaptation of FlowSOM, a pioneering approach that combines self-organizing maps (SOM) and minimal spanning trees (MST), with the fast-greedy network clustering algorithm to unravel intricate relationships within multivariate water quality datasets. By capturing connections within the data, this ensemble tool enhances clustering and pattern recognition. Applied to the complex water quality context of the hyper-arid transboundary Caplina/Concordia coastal aquifer system (Peru/Chile), the FlowSOM network and clustering yielded compelling results in pattern recognition of the aquifer salinization. Analyzing 143 groundwater samples across eight variables, including major ions, the approach supports the identification of distinct clusters and connections between them. Three primary sources of salinization were identified: river percolation, slow lateral aquitard recharge, and seawater intrusion. The analysis demonstrated the superiority of FlowSOM clustering over traditional techniques in the case study, producing clusters that align more closely with the actual hydrogeochemical pattern. The outcomes broaden the utilization of multivariate analysis in water research, presenting a comprehensive approach to support the understanding of groundwater systems.

A Low-Cost IoT Architecture Based on LPWAN and MQTT for Monitoring Water Resources in Andean Wetlands

A Low-Cost IoT Architecture Based on LPWAN and MQTT for Monitoring Water Resources in Andean Wetlands

Ecology of Saline Watersheds: An Investigation of the Functional Communities and Drivers of Benthic Fauna in Typical Water Bodies of the Irtysh River Basin.

In this study, we investigated how changes in salinity affect biodiversity and function in 11 typical water bodies in the Altai region. The salinity of the freshwater bodies ranged from 0 to 5, the brackish water salinities ranged from 5 to 20, and the hypersaline environments had salinities > 20. We identified 11 orders, 34 families, and 55 genera in 3061 benthic samples and classified them into 10 traits and 32 categories. Subsequently, we conducted Mantel tests and canonical correlation analysis (CCA) and calculated biodiversity and functional diversity indices for each sampling site. The results indicated that biodiversity and the proportion of functional traits were greater in freshwater environments than in saline environments and decreased gradually with increasing salinity. Noticeable shifts in species distribution were observed in high-salinity environments and were accompanied by specific functional traits such as swimming ability, smaller body sizes, and air-breathing adaptations. The diversity indices revealed that the species were more evenly distributed in high-diversity environments under the influence of salinity. In contrast, in high-salinity environments, only a few species dominated. The results suggested that increasing salinity accelerated the evolution of benthic communities, leading to reduced species diversity and functional homogenization. We recommend enhancing the monitoring of saline water resources and implementing sustainable water resource management to mitigate the impact of salinity stress on aquatic communities in response to climate-induced soil and water salinization.

The spatiotemporal variation in lead and zinc contents in the Moulouya River (northeastern Morocco)

In the context of the challenges imposed by climate change, which exerts constant pressure on water security in Morocco, the Moulouya River, one of the main sources of surface water, frequently undergoes spatiotemporal variations in its quality. This situation is particularly worrying in the upstream part of the river, where the old Zaida mine is located, which is now inactive and leaves significant residues in the form of slag heaps in direct contact with the waters of the Moulouya River. To assess the impact of this abandoned mining waste, eight (08) water sampling stations (S1 to S8) were chosen to analyse the concentrations of two (02) essential metals, namely, lead and zinc. The results revealed lead concentrations of approximately 0.13 mg/L on average during the dry period and 0.09 mg/L during the humid period. The average zinc concentration was 0.29 mg/L during the dry season and 0.32 mg/L during the humid season. The maximum concentrations were recorded at the stations closest to the aforementioned mine, in particular, S1, S2 and S3. Other maximum concentrations were observed at station S6. These results highlighted the significant impact of mining slag heaps exposed to the open sky on the physico-chemical and metallic quality of Moulouya waters. This finding highlights the urgency of continued monitoring of this vital water resource to protect it against potential risks of contamination.