Monitoring and Assessment of Seasonal Variation of Water Quality Using a Multi-Band Cloud Based Application in Coastal Environment

Remote sensing is commonly utilized for surface cover classification and change analysis. An important approach in studying water resources and assessing hydrological drought involves utilizing remote sensing to extract various land cover features. Given the potential influence of environmental noise, the objective of this study is to devise an index that enhances water extraction accuracy while establishing a stable threshold value. The investigation focuses on the Water Ratio Index (WRI) and the Automated Water Extraction Index (AWEI) in the context of Bath, United Kingdom, particularly addressing areas with shadows and dark surfaces that often lead to misclassifications by other indices. The application and comparative performance assessment of these indices are conducted using GIS and Remote Sensing technology. WRI analysis reveals index values ranging from 0.83 to 1.24, highlighting regions with water or moisture content (WRI greater than 1) and extensive areas devoid of water (WRI less than 1). Notably, AWEI nsh yields more accurate predictions than AWEI sh, which tends to identify shade and man-made surfaces rather than water surfaces. AWEI nsh exhibits a significantly higher water land cover figure (11342.5) compared to AWEI sh’s minimal value (359.5). In scenarios where water information is susceptible to noise, AWEI proves to be a more suitable and effective alternative water index. It is recommended for use in locations with challenging water data, offering improved accuracy and reliability.

The study aims to assess spatial and temporal water quality variations in the upper reaches of the Vietnamese Mekong Delta. Thirty-one water monitoring samples of the two main rivers (Tien and Hau Rivers) and six canals flowing through An Giang Province were collected in the dry season (March) and the rainy season (September) from 2009 to 2019. Seven physicochemical parameters were analyzed including temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), total suspended solids (TSS), orthophosphate (P-PO43-), and coliforms. Water quality index (WQI), cluster analysis (CA), and discriminant analysis (DA) were applied to evaluate water quality, spatial and temporal variations, and seasonal discriminant water variables. WQI values (15–71) indicated surface water quality was very bad to medium in which the water quality in larger and in smaller rivers in the dry season was less polluted than that in the rainy season due to erosion and runoff water containing waste materials in the wet season. CA grouped the water quality in the dry and rainy seasons into four clusters mainly due to BOD and coliforms in the dry season; TSS and coliforms in the rainy season. Discriminant analysis revealed that DO, TSS, coliforms, temperature and BOD significantly contributed to seasonal variations in water quality. Therefore, water quality monitoring in the surveyed area could only focus on DO, TSS, coliforms, temperature and BOD to reduce monitoring cost.

This study evaluates the impacts of construction activities from the Ulu Jelai Hydroelectric Project on the water quality of the Susu Reservoir, a critical freshwater system in Malaysia fed by the Telom, Mensun, Bertam, Menlock, Lemoi, and Tekai rivers. A systematic spatial–temporal analysis was conducted using 15 water quality monitoring stations (WQ1–WQ18), strategically distributed across tributaries, inflow points, and the Susu Dam. Key physicochemical, biological, and hydrological parameters, including turbidity, total suspended solids (TSS), pH, dissolved oxygen (DO), ammonia (NH3-N), E. coli, and oil and grease (O&G), and flow rate, were analyzed across wet and dry seasons. The results revealed significant seasonal variability in water quality parameters. Dry periods were characterized by elevated DO, O&G, and flow rate levels with an average of 8.98 mg/L, 1932.98 mg/L, and 7.48 m3/s, respectively, alongside reduced TSS and E. coli with an average of 300.23 mg/L and 656.47 CFU/100mL, respectively. In contrast, wet seasons exhibited heightened turbidity, BOD, and nutrient influx, with an average of 201.73 NTU, 1.84mg/L, and 0.16 mg/L, due to runoff within the Upper Susu Watershed. Principal component analysis (PCA) attributed dry-season conditions to climatic and physicochemical drivers, whereas wet-season water quality degradation correlated with anthropogenic activities, such as agricultural runoff, livestock operations, and rainfall conditions. Turbidity levels exceeded regulatory thresholds at multiple monitoring stations, underscoring localized sediment mobilization near construction zones. The findings underscore the cumulative impacts of land-use practices and hydroelectric infrastructure on reservoir integrity. Elevated contaminant levels, driven by seasonal hydrological dynamics and anthropogenic pressures, necessitate adaptive management strategies. This study provides a framework for balancing hydroelectric development with ecological sustainability in tropical montane systems, emphasizing evidence-based practices to minimize anthropogenic degradation in freshwater reservoirs.

Spectral indices for detecting surface water bodies play a crucial role in environmental studies, on the other hand, these indices behave differently depending on the study site or the bands used. Therefore, this study proposed an evaluation of the performance of spectral indices in detecting surface water, using a scene from the Landsat-9 satellite and implementing a new index called NIR-Green Water Index (NGWI). After a visual inspection, positive performance was found in all indices in the detection of surface water, especially those formulated using the green band, such as the Normalized Difference Water Index (NDWI), (NGWI) and Water Ratio Index (WRI). During the Pearson correlation and Separability Index analysis between the "Water" and "Non-water" classes, the Automated Water Extraction Index (AWEI) demonstrated the highest average separability, reaching 1.24, and the lowest correlation, with 0.15, which places it as the index with the best detection estimates. The NGWI stood out especially when compared to other indices, showing a moderate behavior with an average separability of 1.15, surpassing the WRI (1.12) and the MNDWI (1.13), in addition to an average Pearson correlation of 0.17, just behind AWEI (0.16) and WRI (0.15). Although all the indices mentioned have demonstrated usefulness in water detection, it was observed that more complex indices, with a more elaborate formulation and less sensitivity to shadows, such as AWEI, produced results comparable to simpler indices, such as NDWI and MNDWI.

Surface water is very vulnerable to pollution due to its ease of accessibility to human and animals, runoff from farmlands and other anthropogenic contaminations. It is one thing to have water within one’s reach, and another to have it potable for use. This paper reports the seasonal variation and implication of the quality status of two surface dams located in some water-stressed communities of Plateau State, North Central Nigeria. Water quality assessments were carried out for a period of six seasons (August 2009–April 2012) using standard analytical methods. Seasonal variation of the water quality showed increasing and decreasing metal ion concentration in the dry and wet seasons, respectively, except for copper, which had a reverse trend. Seasonal values showed that most of the metals had values above their respective WHO standards for drinking water. For average concentrations, Mabudi dam had higher values of lead (0.53±0.02)mg/l, arsenic (0.08±0.001)mg/l, cobalt (0.11±0.001)mg/l and nickel (0.36±0.02)mg/l in the dry season than Wubang dam. Wubang dam recorded higher cadmium (0.80±0.001)mg/l and aluminium (1.79±0.001)mg/l values with no mercury detected throughout the period of study. Other physicochemical parameters also vary between the seasons with most of the values (for turbidity, colour, suspended and total dissolved solids) exceeding their maximum tolerable limits for both seasons. The water in both seasons had high microbial loads, with the dry season being higher. Biochemical tests on the isolates showed the presence of Staphylococcus aureus, Escherichia coli, Proteus spp. Bacillus spp. Salmonella spp. Enterobacterea, Pseudomonas spp. and Shigella, most of whose counts were higher during the dry season. Being the only sources of water for both domestic and agricultural uses in these communities, there is an urgent need for government and non-governmental organizations to address the water situation in the

The aim of this research is the comparison and subsequent evaluation of the suitability of using SAR (Synthetic Aperture Radar) and multispectral (MSI) satellite data of the Copernicus program for mapping and accurate identification of surface water bodies. The paper considers sudden changes caused by significant climatological-meteorological influences in the country. The surface guidance extraction methodology includes the standard preprocessing of SAR images and concluding the determination of threshold values in binary mask generation. For MSI images, water masks are generated through automatic algorithmic processing on the Google Earth Engine cloud platform. During SAR image processing, it has been found that the VV polarization configuration type (vertical-vertical) is the most suitable. The Lee and Lee Sigma filters are recommended for eliminating radar noise. The chosen window size for filtering depends on the specific object and its spatial extent. The extraction of water surfaces from the MSI image is conducted using the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), a pair of Automated Water Extraction Index (AWEI) indices, and Water Ratio Index (WRI). Results are evaluated both graphically and numerically, using quantitative accuracy indicators to refine them. Automatic extraction of water surfaces from MSI images in the GEE platform environment is a fast, efficient, and relatively accurate tool for determining the true extent of groundwater. In conclusion, this research can provide more reliable estimates of hydrological changes and interannual variations in water bodies in the country. When combined with multitemporal monitoring, these results can be an effective tool for permanent monitoring of floods and droughts.The aim of this research is the comparison and subsequent evaluation of the suitability of using SAR (Synthetic Aperture Radar) and multispectral (MSI) satellite data of the Copernicus program for mapping and accurate identification of surface water bodies. The paper considers sudden changes caused by significant climatological-meteorological influences in the country. The surface guidance extraction methodology includes the standard preprocessing of SAR images and concluding the determination of threshold values in binary mask generation. For MSI images, water masks are generated through automatic algorithmic processing on the Google Earth Engine cloud platform. During SAR image processing, it has been found that the VV polarization configuration type (vertical-vertical) is the most suitable. The Lee and Lee Sigma filters are recommended for eliminating radar noise. The chosen window size for filtering depends on the specific object and its spatial extent. The extraction of water surfaces from the MSI image is conducted using the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), a pair of Automated Water Extraction Index (AWEI) indices, and Water Ratio Index (WRI). Results are evaluated both graphically and numerically, using quantitative accuracy indicators to refine them. Automatic extraction of water surfaces from MSI images in the GEE platform environment is a fast, efficient, and relatively accurate tool for determining the true extent of groundwater. In conclusion, this research can provide more reliable estimates of hydrological changes and interannual variations in water bodies in the country. When combined with multitemporal monitoring, these results can be an effective tool for permanent monitoring of floods and droughts.

Shallow wells are vital for water supply in regions lacking centralised systems, but they are highly susceptible to contamination from anthropogenic activities and natural processes. This study investigated seasonal variations in water quality from five shallow wells in Half London Ward, Tunduma, Tanzania, over a 12-month period to assess biological, chemical, and physical parameters, to identify contamination drivers, and to propose sustainable management solutions. Using WHO and EPA guidelines, monthly water sampling was conducted for 12 months from June 2022 to May 2023. Parameters analysed included Faecal and Total Coliforms, Nitrate, Phosphate, Total Iron, Biological Oxygen Demand (BOD), pH, Electrical Conductivity (EC), Turbidity, Total Dissolved Solids (TDS), and Total Suspended Solids (TSS). The National Sanitation Foundation Water Quality Index (NSFWQI) was employed to classify seasonal variations in water quality. Results revealed significant seasonal trends. Microbial contamination peaked during the rainy season, with shallow well WW3 and WW5 recording faecal coliform levels of 5 CFU/100 ml and total coliforms of 18 CFU/100 ml, exceeding WHO and East African Standards. Phosphate levels in shallow well WW4 and WW5 exceeded the threshold of 2.2 mg/l, attributed to agricultural runoff. Elevated iron concentrations (1.85 mg/l) in WW4 reflected natural geological leaching. BOD and turbidity increased during wet periods due to organic pollutants and sediment influx, while physical parameters such as pH and TDS remained within permissible limits. The NSFWQI ranged from "Excellent" (18.6) in shallow well WW5 during winter to "Medium" (65.4) in shallow well WW3 during summer, highlighting contamination risks from surface runoff and poor land management. The study concludes that rainfall and proximity to pollution sources significantly impact shallow well water quality. It recommends implementing community-driven sanitation measures, protecting shallow wells, and conducting routine monitoring. These findings provide a framework for improving groundwater quality for domestic use in urbanising regions globally.

The Ping River, located in northern Thailand, is facing various challenges due to the impacts of climate change, dam operations, and sand mining, leading to riverbank erosion and deposition. To monitor the riverbank erosion and accretion, this study employs remote sensing and GIS technology, utilizing five water indices: the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Soil-Adjusted Vegetation Index (SAVI), Water Ratio Index (WRI), and Automated Water Extraction Index (AWEI). The results from each water index were comparable, with an accuracy ranging from 79.10 to 94.53 percent and analytical precision between 96.05 and 100 percent. The AWEI and WRI streams showed the highest precision out of the five indices due to their larger total surface water area. Between 2015 and 2022, the riverbank of the Ping River saw 5.18 km2 of erosion. Conversely, the morphological analysis revealed 5.55 km2 of accretion in low-lying river areas. The presence of riverbank stabilizing structures has resulted in accretion being greater than erosion, leading to the formation of riverbars along the Ping River. The presence of water hyacinth, narrow river width, and different water levels between the given periods may impact the accuracy of retrieved river areas.

Longitudinal water quality monitoring is important for understanding seasonal variations in water quality, waterborne disease transmission, and future implications for climate change and public health. In this study, microfluidic quantitative polymerase chain reaction (MFQPCR) was used to quantify genes from pathogens commonly associated with human intestinal infections in water collected from protected springs, a public tap, drainage channels, and surface water in Kampala, Uganda, from November 2014 to May 2015. The differences in relative abundance of genes during the wet and dry seasons were also assessed. All water sources tested contained multiple genes from pathogenic microorganisms, with drainage channels and surface waters containing a higher abundance of genes as compared to protected spring and the public tap water. Genes detected represented the presence of enterohemorrhagic Escherichia coli, Shigella spp., Salmonella spp., Vibrio cholerae, and enterovirus. There was an increased presence of pathogenic genes in drainage channels during the wet season when compared to the dry season. In contrast, surface water and drinking water sources contained little seasonal variation in the quantity of microbes assayed. These results suggest that individual water source types respond uniquely to seasonal variability and that human interaction with contaminated drainage waters, rather than direct ingestion of contaminated water, may be a more important contributor to waterborne disease transmission. Furthermore, future work in monitoring seasonal variations in water quality should focus on understanding the baseline influences of any one particular water source given their unique complexities.

Water management is a significant challenge, stimulating synergies between scientists and practitioners to create new tools and approaches to streamline decision making in this field. The assessment and monitoring of freshwater quality in surface water bodies are crucial for sustainable and safe water management. The main objectives of this study were to analyze the characteristics and properties of Chirita lake, assess seasonal variations in water quality, determine compliance with national environmental legislation, and perform a comparison with monitoring systems in other European lakes. The study used data that determined water quality indicators for a five-year period, from 2020 to 2024, considering temperature, turbidity, pH, conductivity, alkalinity, hardness, organic matter, nitrates, nitrites, ammonium, and chlorides. The statistical analysis technique based on the Pearson correlation coefficient was used to evaluate the seasonal correlations of water quality parameters in Chirita lake and to extract the essential parameters for assessing seasonal variations in river water quality. The results obtained indicated that the indicators considered important for water quality variation in one season may not be important in another season, except for organic matter and conductivity, which showed a significant contribution to water quality variation throughout the four seasons. This study demonstrated that lake water is classified as first class, according to national regulations. These results provide valuable support for local authorities to develop effective strategies for water quality management and the prevention of eutrophication processes in reservoirs.

Seasonal variation in surface water quality of Dal Lake was assessed using multivariate statistical techniques.Water quality data collected from 4 sampling sites during 4 seasons was analysed for 13 parameters.Significant variation (p<0.05) in temperature, pH, EC, Ca, Mg, Na, K, DO, BOD 5 , COD, PO 4 -P, NH 4 -N and NO 3 -N of sampling sites during different seasons was observed.Cluster analysis grouped 4 sampling sites during 4 seasons into three clusters of similar water quality as relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites.The principal component analysis/factor analysis applied to extract and recognize the factors responsible for water quality variations in four seasons of the year resulted in three principal components for each season accounting for 75.78%, 83.25%, 87.33% and 78.96% of total variance for winter, spring, summer and autumn seasons respectively.Parameters like NH 4 -N, NO 3 -N, BOD 5 and COD have strong positive loading whereas temperature and dissolved oxygen has strong negative loading.Thus, from the principal component/factor analysis it is clear that the domestic wastewaters, agricultural runoff and catchment geology play significant role in water quality variations in the Dal Lake for all the four seasons.

Water pollution has become a growing threat to human society and natural ecosystems in recent decades. It increases the need to understand surface water quality assessment better using chemometric tools within aquatic systems. This study sampled the water quality of 21 parameters at multiple sampling points in Jabi Lake during wet and dry seasons (August–December 2021) using various statistical methods including cluster analysis, principal component analysis/factorial analysis, discriminant analysis, and box plot analysis. These samples were examined for physicochemical parameters employing standard techniques. The study revealed significant seasonal variations in water quality. During the wet season, key measurements included total dissolved solids (100.40 mg/l), dissolved oxygen (13.72 mg/l), and electrical conductivity (97.14 µs/cm). The dry season showed higher levels of most parameters, with total dissolved solids at 137.91 mg/l and electrical conductivity at 230.93 µs/cm. Statistical analysis identified strong correlations between various parameters, notably between phosphate and total hardness in the wet season (r = 0.978, α = 0.05) and between pH and temperature in the dry season (r = 0.995, α = 0.05). The study identified four principal components explaining 98.5–100% of the variance, representing various pollution sources including organic waste, domestic sewage, and natural factors. The findings indicated that dry season water samples were more polluted, with some parameters exceeding World Health Organisation standards, suggesting potential health risks. The research demonstrated the effectiveness of multivariate statistical techniques in analysing complex water quality data and provided valuable insights for water resource management, particularly regarding seasonal variations' impact on water quality.

This study assesses the availability and quality of water sources in Ubani (urban) and Ndoro (rural) markets in Abia State, Nigeria, focusing on both physicochemical and bacteriological properties. The findings have important implications for Water, Sanitation, and Hygiene (WASH) practices. A mixed-method approach was employed, combining quantitative (water sampling, laboratory analysis), and qualitative (a structured questionnaire) techniques to assess water availability, quality, and usage patterns. Water samples were collected from three sources (borehole, rainfed wells, and tanker-supplied water) during dry and wet seasons. Physicochemical parameters analyzed included pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), nitrate, and Dissolved Oxygen (DO). Bacteriological assessments detected coliforms, Escherichia coli, and Klebsiella spp. using the Most Probable Number (MPN) method. Sachet water was the most widely used water source, with 81.1% of respondents in Ubani and 77.1% in Ndoro relying on it, yet more than half reported difficulties in accessing water. Distances to water sources in the market varied, ranging from 250m to 1.5km. Seasonal variations in water quality were significant (p &lt; 0.05). In Ubani market, BOD increased from 4.82 mg/L in the dry season to 9.07 mg/L in the wet season, while DO rose from 2.78 mg/L to 4.63 mg/L. Similar trends were observed in Ndoro, with BOD rising from 4.80 mg/L to 8.52 mg/L. The TDS levels in Ubani market were 174.5mg/l in the dry season and 192.63mg/l in the wet season whereas that of Ndoro market was 44mg/l and 29mg/l. The pH in Ubani was 6.68 (dry) and 6.11 (wet), while Ndoro recorded 3.96 (dry) and 4.55 (wet). Nitrate levels also fluctuated significantly between seasons. Bacteriological analysis revealed E. coli concentrations of 1.72 × 10⁵ cfu/ml in Ubani's rainwater and Klebsiella spp. counts of 2.2 × 10⁵ cfu/ml in Ndoro's borehole water, exceeding WHO limits. The findings of the study reveal the vulnerability of market water sources to contamination, especially during the wet season, posing public health risks. The study highlights the urgent need for water quality monitoring, treatment improvements, and public health education on safe water practices.

Land use, hydrology, and climate influence water quality of China's largest river

Agriculture has both direct and indirect effects on the quality of surface water and groundwater and is among the leading causes of water quality degradation, mainly as a result of the excessive use of agrochemicals. Water samples were collected in a selected catchment area (Bonsma Dam) in KwaZulu-Natal and analysed for physicochemical variables. The concentrations of most of the elements and total dissolved solids, as well as the pH and electrical conductivity values, met the water quality requirements for domestic, agricultural, livestock and aquatic ecosystem uses. However, the inlet streams feeding the dam were found to be eutrophic during the wet season. Analysis of nitrate in the water body of the study area indicated that agricultural applications of manure and fertilisers may be a potential source of nitrate contamination. Most elements were more concentrated in the dam during the wet season. The overall ionic conductivity values were also higher in the wet season, while the pH was lower. The outcome of this work links the concentrations of physicochemical variables to land use, agricultural practice and local geomorphology. Seasonal patterns in the concentration of physicochemical variables occur, as land use, rainfall and farming activities change seasonally, and these concentrations should therefore be determined periodically.