Reservoir Water Quality Research Articles

Water quality benefits of implementing Green and Blue Infrastructure in a peri-urban catchment – Case study of a Brazilian metropolis

This paper investigates the effectiveness of Green and Blue Infrastructure (GBI) in managing non-point source pollution in the 120 km2 catchment of the Vargem das Flores reservoir, which supplies water to 600,000 inhabitants in the Belo Horizonte metropolitan area, Brazil's third-largest metropolis. A calibrated water quality model within the Storm Water Management Model (SWMM) was used to simulate total suspended solids (TSS) and nutrients loads under different future land use scenarios. The first scenario reflects maximum urban development according to current legislation. An alternative scenario was proposed considering the protection of forest remnants and the adoption of the most cost-effective GBI techniques in newly developed areas. Results indicated a substantial contribution of diffuse pollution to the reservoir's water quality degradation, mainly due to TSS loads transported from the most urbanized subcatchments. The application of bioretention cells to treat 12.6 km2 new impervious areas effectively mitigated diffuse pollution, removing 52.1% of TSS, 19.5% of total phosphorus (TP), and 17.1% of chemical oxygen demand (COD) loads flowing into the reservoir. The GBI implementation would maintain pollutant loads similar to current levels despite the increased urbanization.

An uncertainty‐based evaluation of sulphuric compounds transport in reservoirs considering sensitivity analysis

AbstractDeepwater bodies such as dam reservoirs are among the most prominent water resources that supply societies' water demand. Hence, water quality in these resources requires continuous and meticulous monitoring. This study evaluated the water quality of Changuleh Dam, a dam under construction in western Iran, using the CE‐QUAL‐W2 model to predict fluctuations in sulphate (SO42−) and hydrogen sulphide (H2S) concentrations. A sensitivity analysis was conducted to assess the dependence of model outputs on input variables. Additionally, an uncertainty analysis was performed using MATLAB to address the inherent uncertainties of the input variables through an ARIMA (2, 2, 1) time series model and Monte Carlo simulations. The results indicated a high sulphate concentration averaging 955.6 mg/l, considerably exceeding the drinking water standard of 400 mg/l, identifying the reservoir as a sulphate‐rich water resource. Seasonal fluctuations of H2S concentrations were also observed in the reservoir's anaerobic bed sediments, peaking at 87 mg/l. The sensitivity analysis showed that nitrate and total dissolved solids (TDS) varied more noticeably than dissolved oxygen (DO) at different depths, with the greatest variation at 50 m. This study highlights the critical need for continuous and meticulous monitoring of water quality in dam reservoirs to meet water supply standards.

Analisis Kualitas Air Embung Bendo, Mayangkawis dan Babo dengan Peruntukan Wisata dan Pertanian di Kabupaten Bojonegoro

With the potential of the agricultural sector, Bojonegoro Regency certainly requires an abundant supply of clean water. To overcome this problem, the Bojonegoro Regency Government built reservoirs to protect clean water supply in several areas in Bojonegoro Regency. The utilization of existing embung in Bojonegoro is not limited to agriculture only, but also as a tourist spot. To date, there has been no research assessing the quality of water in reservoirs. Consequently, this study aimed to evaluate the water quality in three reservoirs located in Bojonegoro. This study employs qualitative analysis derived from the environmental laboratory water quality assessments conducted by Dinas Lingkungan Hidup Kabupaten Bojonegoro. The findings from the water analyses of Embung Bendo, Embung Mayangkawis, and Embung Babo reveal distinct conditions among the three locations. These variations are attributed to differing environmental factors that influence each site uniquely.

Improving the accuracy of an integrated watershed-reservoir water quality modeling approach (SWAT and CE-QUAL-W2) in data scarce watersheds

Watershed-reservoir modeling is an essential tool in lake and reservoir management. Despite its importance, accurately modeling these systems remains challenging due to their inherent complexity and the scarcity of reliable forcing data. This study focuses on a watershed and reservoir located in northern Portugal, where data scarcity presents significant challenges for accurate modeling. Therefore, the primary goal of this study was to improve the prediction capability of an integrated watershed-reservoir water quality modeling solution and to establish a robust methodology that can be effectively applied to other cases. The modelling solution proposed in this study computes a daily ratio (per water quality variable) between the watershed reference and load reduction modeling scenarios, obtained with the SWAT model, which is then applied to the observed forcing water quality data of a reservoir water quality model (CE-QUAL-W2). The load reduction scenario increases the watershed municipal wastewater treatment plant’s efficiency from secondary to tertiary and applies a haul-off scenario to the livestock manure, reducing the reservoir input load of Total P, Total N, and BOD5 in the reservoir by 87%,86%, and 83%, respectively. The results obtained with the classical model solution provided an inaccurate conclusion regarding the load reduction scenario output that was corrected with the proposed modeling approach. Overall, after the correction, the load reduction scenario served to considerably improve the reservoir water quality by reducing the Total P and the phytoplankton biomass by 39% and 72%, respectively, while increasing the DO concentration by 27%. The results of this study show that the proposed modeling approach improves the performance of integrated watershed-reservoir modeling systems in data scarce settings and can serve as a useful tool to support decision-making with regard to water resource management and planning at regional and national level.

Climate-driven decline in water level causes earlier onset of hypoxia in a subtropical reservoir

Hypoxia, especially in the bottom water, is occurring in deep and stratified reservoirs worldwide, threatening aquatic biodiversity, ecosystem functions and services. However, little is known about the timing of onset and ending of hypoxia, especially in subtropical reservoirs. Based on five-year (from April 2015 to January 2020) sampling of a subtropical monomictic deep reservoir (Tingxi Reservoir) in southeast China, we found the evidence of about 40 days earlier onset of hypolimnion hypoxia during low water level periods in dry years compared to wetter high water level years. We explored the effects of stratification and mixing conditions on hypoxia, cyanobacterial biomass, and nutrient dynamics; and revealed the physical and biochemical conditions that drove hypoxia. The results indicated that 1) The decline in water level increased the intensity of thermal stratification, resulting in 40 days earlier onset of hypolimnion hypoxia in dry years than in wet years; 2) The decline in water level expanded the extent of hypoxia by promoting nutrient accumulation and phytoplankton biomass growth; 3) Warmer climate and less precipitation (drought) significantly promoted the risk of hypoxic expansion and endogenous phosphorus release in subtropical reservoirs. We suggest that more attention needs to be paid to the early onset of hypoxia and its consequences on water quality in subtropical stratified reservoirs during low water level periods in a changing climate.

Water Quality Identification and Analysis of Saguling Reservoir, West Bandung Regency

The Saguling Reservoir in West Bandung Regency is crucial for local water supply and biodiversity. However, water quality has significantly declined due to human activities, environmental changes, and poor waste management. This degradation jeopardizes the reservoir's clean water supply and the ecological health of surrounding communities. This research used the STORET (Storage and Retrieval) method to assess water quality through purposive sampling in the most affected zones, analyzing physical, chemical, and biological parameters at multiple depths. Key indicators, such as dissolved oxygen, nutrient concentrations, and heavy metal presence, were measured. Results show severe water quality deterioration, especially in deeper sections with less sunlight and aeration. Elevated nitrate and phosphate levels, mainly from agricultural runoff and residential waste, exceeded environmental safety standards. Toxic heavy metals like lead and mercury were also detected, posing serious risks to aquatic life and human health. The study highlights that deeper water zones are critically impacted by pollutants, stressing the need for better regulatory frameworks and waste management. Immediate strategic interventions are crucial to restore and protect the Saguling Reservoir's water quality for future generations.

Analysis of The Effect of Floating Net Cages on Reservoir Water Quality

The use of Darma Waduk is as a source of clean water and a place of fish cultivation in the form of net cages. The number of floating net cages that currently exist is 6521, and the number continues to increase quite significantly from the previous year. The existence of floating net cages affects water quality, while water quality is an aspect to be considered in water utilization. The study aims to analyze the impact of floating net cages on water quality and efforts to control the floating net cage population in Darma Waduk. The research uses a quantitative and qualitative approach, starting with surveys and interviews with floating net cage farmers and related services. Subsequently, water samples were taken, and the results of laboratory tests were analyzed using the Pollution Index (IP) method. The results of the calculation using the pollution index (IP) method at the first point (PDAM Intake Area) obtained a result of 4.14, at the second point (around the floating net cage area of the village of Cipasung) a value of 3.76, and at the third point (in the floating net cage village of Jagara) a score of 4.12. The results of the research showed that the water quality taken from the three points of the sample included light pollution when referring to the class I water quality standard according to PP No. 22 for 2021. However, the presence of household and agricultural waste also contributes to the pollution burden in the Darma Valley.

Analysis and evaluation of development characteristics of debris flow in Longshougou

Long Shougou is located at the exit of Hei he River in Zhang ye City, which is directly facing the reservoir of Long Shougou Hydropower Station. Through site survey, UAV flight and indoor simulation calculation, the development characteristics and risk of Long Shougou debris flow were analyzed and evaluated. The results show that: Long Shougou debris flow is in the "development period", belonging to "small", "easy" and "high frequency" debris flow; The length of Long Shougou Channel is 1.70 km, the circumference is 3.76 km, the area is 0.72 km2 , the relative height difference is nearly 584 m, the longitudinal gradient is 342.27 ‰, and the overall bending coefficient is 1.14; Gully sediments are mainly gully bed deposits and slope collapse deposits, with total reserves of 20.94 ×104 m3 and dynamic reserves of 7.76 ×104 m3 ; Calculated according to the designed rainstorm frequency of 1%, the flow velocity of debris flow at the gully mouth is 2.64m /s, the total amount of primary overflow is 17,296.41m3 , the primary outflow of solid matter is 7956.35m3 , and the overall impact pressure of debris flow is 16.32kPa. The maximum impact force of a single stone is 2.56kN according to 0.5m; It is suggested to consider the impact of the annual inflow of debris flow on the water quality of the Longshou hydropower station reservoir and the impact on the power station switching station near the Gulou port, and do a good job in the relevant sediment discharge measures.

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy

In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area. Since traditional procedures to collect water quality data are time-consuming, labor-intensive, and costly, water resources management has shifted from gathering field measurement data to utilizing remote sensing (RS) data. WDD has been threatened by various driving forces in recent years, such as contamination from different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, and microbial contamination. Therefore, this study aimed to retrieve and map WQIs, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a) of the Wadi Dayqah Dam (WDD) reservoir from Sentinel-2 (S2) satellite data using a new procedure of weighted averaging, namely Bayesian Maximum Entropy-based Fusion (BMEF). To do so, the outputs of four Machine Learning (ML) algorithms, namely Multilayer Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVRs), and XGBoost, were combined using this approach together, considering uncertainty. Water samples from 254 systematic plots were obtained for temperature (T), electrical conductivity (EC), chlorophyll-a (Chl-a), pH, oxidation–reduction potential (ORP), and dissolved oxygen (DO) in WDD. The findings indicated that, throughout both the training and testing phases, the BMEF model outperformed individual machine learning models. Considering Chl-a, as WQI, and R-squared, as evaluation indices, BMEF outperformed MLR, SVR, RFR, and XGBoost by 6%, 9%, 2%, and 7%, respectively. Furthermore, the results were significantly enhanced when the best combination of various spectral bands was considered to estimate specific WQIs instead of using all S2 bands as input variables of the ML algorithms.

Water quality ensemble prediction model for the urban water reservoir based on the hybrid long short-term memory (LSTM) network analysis

ABSTRACT The water quality of drinking water reservoirs directly impacts the water supply safety for urban residents. This study focuses on the Da Jing Shan Reservoir, a crucial drinking water source for Zhuhai City and the Macau Special Administrative Region. The aim is to establish a prediction model for the water quality of drinking water reservoirs, which can serve as a vital reference for water plants when formulating their water supply plans. In this research, after smoothing the data using the Hodrick-Prescott filter, we utilized the long short-term memory (LSTM) network model to create a water quality prediction model for the Da Jing Shan Reservoir. Simulation calculations reveal that the model's fitting degree is consistently above 60%. Specifically, the prediction accuracy for pH, dissolved oxygen (DO), and biochemical oxygen demand (BOD) in the water quality prediction model aligns with actual results by more than 70%, effectively simulating the reservoir's water quality changes. Moreover, for parameters like pH, DO, BOD, and total phosphorus, the relative forecasting error of the LSTM model is less than 10%, confirming the model's validity. The results of this study offer an essential model reference for predicting water quality for the Da Jing Shan Reservoir.

Evaluating impacts of climate and management on reservoir water quality using environmental fluid dynamics code

Climate change and human interference, notably nutrient input, affect the water quality. Nitrogen (N) and phosphorus (P) are pivotal in managing eutrophication. This study investigated the effects of water dynamics and chemical constituents on water quality in Hongfeng Lake, a typical weakly stratified reservoir suffering from algae blooms in Southwest China, using the Environmental Fluid Dynamics Code. Leveraging climate, hydrological, and water quality data, we constructed, calibrated, and validated the temperature-hydrodynamics-water quality-sediment model. Various scenarios were analyzed, including wind speed, air temperature, solar radiation, rainfall, water discharge, N and P external input, and internal release. The findings revealed that no rain and warming increased trophic state index (TSI) and chlorophyll-a (Chl-a) concentration, and no solar radiation initially elevated nitrate concentration, followed by an increase in ammonium concentration. Besides, no solar radiation and changes in rainfall significantly increased total phosphate concentration. The management scenarios of N and P reduction, halving tributary, and mainstream flow scenarios improved water quality and reduced eutrophication. The wind speed under the N and P reduced scenarios showed that a doubling in wind led to increased concentrations of the particulate organic matter, Chl-a, and dissolved oxygen, alongside decreased ammonium and nitrate, while TSI exhibited minimal change. However, 5- and 10-times wind speed scenarios amplified TSI in shallow water, potentially due to a substantial rise in internal nutrient release. The degradation trend observed in drinking water quality amid climate change (warming and flooding) raises concerns regarding health-related risks. These simulations provided the quantified influence of climate change and environmental management strategies on water quality in the weakly stratified reservoir, notably highlighting the looming threat of exacerbated eutrophication due to warming, necessitating more stringent N and P reduction measures compared to current practices.

Plankton Community Stability and Drive Factor Identification Guizhou Plateau Reservoir

In order to understand the stability of the zooplankton and phytoplankton communities in the Guizhou plateau reservoir environment, the process of reservoir water quality change affecting the stability of plankton was studied. The changes in the plankton community and water quality in three different nutrient reservoirs （Huaxi Reservoir, Goupitan Reservoir, and Hailong Reservoir） were studied from October 2020 to August 2021. The stability of the zooplankton and phytoplankton communities was studied using time-lag analysis （TLA）. Variance decomposition analysis （VPA） was used to explore the response of the two communities to environmental changes. The driving factors of plankton community changes in reservoirs were also revealed. The results showed that Huaxi Reservoir and Goupitan Reservoir were mesotrophic reservoirs, and Hailong Reservoir was a eutrophic reservoir. The average comprehensive nutrition indices of the three reservoirs were 44.07, 44.68, and 50.25. A total of 51 species of zooplankton rotifers, 39 species of rotifers, three species of copepods, and nine species of cladocera were identified. Among them, the abundance of rotifers was the highest, accounting for 85.96%. A total of seven phyla and 73 species of phytoplankton were identified, including 16 species in the phylum Cyanophyta, 32 species in the phylum Chlorophyta, 16 species in the phylum Diatoma, three species in the phylum Chlorophyta, four species in the phylum Euglenophyta, and one species each in the phyla Cryptophyta and Chrysophyta. Among them, the abundance of cyanobacteria and diatoms was the highest, accounting for 66.2% and 27.35%, respectively. The median absolute deviation （MAD） of the Bray-Curtis distance of zooplankton and phytoplankton community in the three reservoirs were 0.67 and 0.65 in Huaxi Reservoir, 0.80 and 0.69 in Goupitan Reservoir, and 0.85 and 0.47 in Hailong Reservoir, respectively. The larger the value, the greater the variation in the community. The absolute value of the slope of zooplankton was greater than that of phytoplankton in the TLA results, and the absolute values of the slopes were 0.018 and 0.004, respectively. The larger the absolute value of the slope, the faster the community variability. The zooplankton community in the three reservoirs was less stable than the phytoplankton community and more sensitive to environmental changes, and the degree of variation was greater. The higher the degree of eutrophication of the reservoir, the more obvious this phenomenon. VPA showed that the changes in plankton communities in Huaxi Reservoir and Hailong Reservoir were mainly influenced by water temperature and eutrophication factors. The changes in planktonic community in Goupitan Reservoir were mainly influenced by water temperature and chemical factors. The driving factors of Huaxi Reservoir were water temperature, TP, permanganate index, and SD. The driving factors of Goupitan Reservoir were water temperature, NO3-- N, and pH. The driving factors of Hailong Reservoir were water temperature and TP. Nutrients and water temperature were the main factors affecting the stability of plankton communities in reservoirs.

Analysis of the Influence of Tambakrigagadung Landfill Leachate Characteristics on Water Quality and Heavy Metal Contamination of Reservoirs, Tikung District, Lamongan Regency

The Final Disposal Site (TPA) of Tambakrigadung Village, Lamongan Regency, implements a semi-sanitary landfill system in which waste processing still piles up piles of waste with soil for a certain period, causing a foul odor due to the leachate that collects. Leachate that is not managed correctly is feared to flow into reservoirs as irrigation and productive land that is used by local communities for rice fields or ponds around the Tambakrigadung TPA so that it can disrupt workers, reduce the quality and yield of rice fields, and affect the development of pond fish around the Tambakrigadung landfill. This research aims to test the quality of reservoir water, which refers to the leachate water quality standards of PermenLHK RI No. P.59 Tahun 2016. It analyzes the influence of leachate on reservoir water around Tambakrigadung. The method used in this research is grab sampling. Analysis of pH, BOD, COD, and TSS parameters using simple linear regression quantitative analysis. Quantitative parameter analysis shows that TPA Tambakrigadung leachate influences the pH and TSS parameters of reservoir water in Tikung District, Lamongan Regency, which is indicated by a significance value of <0.05. The analysis of heavy metal parameters shows that the leachate does not meet quality standards, with a total N of 187.7 ppm and cadmium of 0.15 ppm. The mercury parameter meets quality standards, namely 0.00065 ppm. Meanwhile, heavy metal parameters in reservoir water have met quality standards based on PP No. 22 of 2021.

Dynamic analysis of non-point source pollution in the Sanhekou Reservoir watershed: Implications for sustainable water management

Exogenous pollution, especially non-point source pollution runoff during a flood season, is an important factor affecting the water quality of reservoirs. The Sanhekou Reservoir (Shaanxi, China) is a newly dammed water body, so its non-point source pollution characteristics are unknown. In this study, three major tributaries of the Sanhekou Reservoir watershed were sampled during five rainfall-runoff events in the 2022 flood season. This enabled study of the transport processes of nitrogen, phosphorus, and suspended particles in the runoff under different rainfall conditions. Using hysteresis and redundancy analyses, the sources and transport modes of pollutants are explored here, alongside analysis of rainfall and hydrological characteristics that affect pollutant concentrations, and exploration of land-use relationships. The results showed that (i) nitrate nitrogen and particulate nitrogen were the main forms of nitrogen output, and particulate phosphorus was the main form of phosphorus output. (ii) Total nitrogen, suspended particles, total phosphorus, and particulate phosphorus were significantly affected by maximum rainfall intensity, average rainfall intensity, and average flow rate, whereas nitrate nitrogen and ammonia nitrogen were significantly affected by rainfall duration, rainfall amount, and rainfall-runoff duration. (iii) If 70 % of barren land and cultivated land with slope >15° were converted to forests, total nitrogen, total phosphorus, and suspended particulate loads in the Sanhekou Reservoir would be reduced during the flood season by 10.37 %, 33.01 %, and 45.39 %, respectively. The factors influencing migration and transformation of pollutants during rainfall runoff are important for operational scheduling decisions of reservoirs and guaranteeing quality of the water supply.

Optimization of Reservoir Water Quality Parameters Retrieval and Treatment Using Remote Sensing and Artificial Neural Networks

Inland water bodies are critical ecosystems that serve several functions including the provision of freshwater, regulation of climate and hydrological flows, and pollution control. Therefore, effective monitoring and management of these water resources is critical for sustainable water supply systems. This study evaluated the possible use of satellite data to estimate water quality parameters (WQPs) in an inland water body. The study also used artificial neural network (ANN) models in addition to the satellite data to determine the optimum coagulant dose for water treatment. The use of earth observations and machine learning methods has not been done extensively in developing countries, specifically, in water quality monitoring and management. The study utilized empirical multivariate regression modelling (EMRM) of the spectral reflectances from satellite data for the retrieval of Chla-a, Turbidity, and total suspended solids (TSS) concentrations in an inland water body. Using MLP-ANN modelling, the extracted spectral reflectance values from the selected sampling points in the reservoir were used as model inputs for the prediction of treated WQPs. A second MLP-ANN model was developed to predict the optimum coagulant dose required for raw water treatment. The R2 values achieved with AN model 1 were 0.81, 0.76, and 0.81 respectively for TSS, turbidity, and Chl-a, and 0.99 for the optimum coagulant dose. The study concluded that spectral reflectance from medium resolution satellite data products can be used to estimate WQPs from inland water bodies. Further, the ANN models demonstrate that extracted water quality data from satellite images can be used to inform ANN models for water quality predictions, and for the optimization of water treatment plant operations.

Examining chlorophyll-a concentrations in tropical reservoirs under various land use changes using Sentinel – 2 and Google Earth engine – Bhadra and Tungabhadra, India

The understanding of spatio-temporal variation in land use and land cover (LULC) patterns is crucial for managing catchment land use planning, as it directly influences of tropical reservoir water quality and the subsequent Nutrient Contamination (NC) of unmonitored water bodies. The current research attempts to accurately measure the influence of LULC and its associated determinants on the quantities of NC loads by using Chl-a as a proxy, within tropical reservoirs, i.e. Bhadra and Tungabhadra, located in same river catchment. This Chl-a spread calculated by the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data products covering the period from July 2016 to June 2021 were done using Google Earth Engine (GEE) platform. The validation analysis confirms the robustness of the methodology with a strong correlation between MCI-calculated values and EOMAP (Earth Observation and Environmental Services Mapping) Chl-a (μg/L) data points for both reservoirs, Bhadra (R2 = 0.64) and Tungabhadra (R2 = 0.68). The findings reveal that, Tungabhadra reservoir consistently exhibits an excessive spatial distribution of Chl-a spread area (17 km2 to 335 km2), reflecting nutrient-rich water inflows, particularly evident during the post-monsoon period. This notable rise could be linked to harvesting the Kharif crop, resulting in elevated nutrient concentrations. In contrast Bhadra reservoir, dominated by forested areas, maintains relatively lower Chl-a spread areas (<20 km2), highlighting its pivotal role in maintaining water cleanliness and serves as a riparian boundary. In addition, the changes in LULC classes show a strong relationship with variation in Chl-a during the studied period, for the Bhadra Reservoir R2 = 0.51 (F- statistics = 3.983, p = 0.021), and the Tungabhadra Reservoir R2 = 0.802 (F- statistics = 7.489, p = 0.0143). This highlights how changes in land use significantly shape contamination dynamics, deepening our understanding of nutrient inputs and contamination drivers in tropical reservoirs.

A novel method for assessing water quality status using MODIS images: A case study of large lakes and reservoirs in China

The changing climate and economic development have exerted significant pressure on the water quality of global lakes and reservoirs (hereinafter referred to as lakes). Existing sporadic in-situ monitoring limits a comprehensive understanding of water quality changes at large spatiotemporal scales. Although remote sensing techniques offer efficient water quality observations, they have primarily focused on monitoring single optical active substances, making it difficult to assess water quality changes caused by chemical indicators such as total phosphorus and total nitrogen. However, changes in chemical indicators within a certain range can cause responses to physical indicators, which are comprehensively reflected in the water-leaving reflectance. Based on this physical mechanism, this study obtained the water quality status categories (from excellent to severe pollution) by calculating the water quality index (WQI) and developed a new remote sensing assessment model of water quality status using moderate resolution imaging spectroradiometer (MODIS) imagery. This model, combining fractional-order derivatives and various-dimensional spectral indices, significantly enhanced the sensitivity of spectral reflectance to water quality states, achieving a maximum correlation of 0.72. To address the applicability of the model at large spatiotemporal scales, this study proposed a classification modeling scheme based on the optimal optical water categories, resulting in a 20.69%–34.48% improvement in model accuracy after classification. Additionally, this study proposed an improved ensemble learning model combining the concepts of Stacking and Bagging, achieving an average accuracy of 82% and enhancing accuracy by 5%–15% compared to the original single model. Subsequently, the model was used for the first time to assess the water quality status of 180 large lakes in China from 2000 to 2022. The results revealed that 76.11% of the lakes exhibited excellent and good water quality, with a spatial distribution pattern showing a “better in the west, worse in the east” pattern. Over the 23-year period, 28.33% and 58.89% of the lakes showed improvement and stability trends in water quality status, with stability and improvement predominating in the western and eastern regions, respectively. The research results provide technical support for the rapid assessment of water quality status and sustainable resource management, highlighting the potential of remote sensing technology in water quality monitoring.

Temporal and spatial variation in water quality in the Son La hydropower Reservoir, Northwestern Vietnam

The Son La hydropower reservoir (S.L.R.) is the largest water reservoir in Vietnam. Da River water has been treated for drinking and domestic purposes; water quality management is essential for the safety of ecosystems and human health. This study was conducted to determine changes in water quality indicators [pH, dissolved Oxygen (D.O.), total suspended solids (T.S.S.), chemical oxygen demand (C.O.D.), ammonium (NH4+), nitrite (NO2-), and coliform] in the Da River in 2010 and the Son La hydropower reservoir during 2014-2023. The results of mean annual values of Da river water quality and Son La hydropower reservoir were, specifically: pH (7.8; 7.4), D.O. (4.3; 6.2), T.S.S. (112; 5), C.O.D. (15; 8.7), NH4+ (0.17; 0.3), NO2- (0.009; 0.04), and coliform (1,723; 747). Water quality parameters significantly varied between rive and reservoir water: D.O., T.S.S., C.O.D., and Coliform. pH, T.S.S., and C.O.D. slightly decreased; however, Dissolved oxygen (D.O.), NH4+, NO2-, and coliform demonstrated an increasing trend during 2014-2023. The impact of the Son La Dam (S.L.D.) on water quality was relatively straightforward: increasing the concentration of dissolved oxygen and the self-cleaning ability of pollutants. Periodic water impoundment was divided (April to August) into a low water level of 175 m, impoundment (January to March), a median water level of 190m, and a high water level of 215 m (September to December) to period. However, the impact of the staged impoundment on water quality, especially in 2014-2023, remains unclear, except D.O., T.S.S., NH4+, NO2- and Coliform exceeded limits or were lower is not significant for living water under the Vietnam regulation, specifically: D.O. (5.36, 5.52; ≥ 6), T.S.S. (25.13; ≤ 25), NH4+ (0.3331; 0.3), NO2- (0.0504; 0.05), coliform (1,018.5; ≤ 1,000). Results from the current study provide valuable information for reservoir and river water pollution source management and reduce potential risks to exposed ecosystems, livelihoods, and human health.

Drinking water safety improvement and future challenge of lakes and reservoirs

To meet the Sustainable Development Goal (SDG) target 6.1, China has undertaken significant initiatives to address the uneven distribution of water resources and to enhance water quality. Since 2000, China has invested heavily in the water infrastructure of numerous reservoirs, with a total storage capacity increase of 4.704 × 1011 m3 (an increase of 90.8%). These reservoirs have significantly enhanced the available freshwater resources for drinking water. Concurrently, efforts to improve water quality in lakes and reservoirs, facilitated by nationwide water quality monitoring, have been successful. As a result, an increasing lakes and reservoirs are designated as centralized drinking water sources (CDWSs) in China. Among the 3,441 CDWSs across all provinces, 40.8% are sourced from lakes and reservoirs, 32.6% from rivers, and 26.6% from groundwater in 2023. Notably, from 2016 to 2023, the percentage of lakes and reservoirs categorized as CDWSs has increased consistently across all 29 provinces. This progress has enabled 561.4 million urban residents to access improved drinking water sources in 2022, compared to 303.4 million in 2004. Our findings underscore the pivotal role of water infrastructure construction and water quality improvement jointly promoting lakes and reservoirs as vital drinking water sources. Nevertheless, the nationwide occurrence of algal blooms has surged by 113.7% from the 2000s to the 2010s , which is a considerable challenge to drinking water safety. Fortunately, algal blooms have been markedly alleviated in past four years. However, it is still crucial to acknowledge that lakes and reservoirs face the challenges of algal blooms, and associated toxic microcystin and odor compounds.

Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD+ sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir. Second, the dataset is segmented into homogeneous clusters based on DO and Chl-a parameters by leveraging an optimized K-means algorithm, facilitating precise estimations. Third, ten sophisticated variational-individual data-driven models, namely generalized regression neural network (GRNN), random forest (RF), gaussian process regression (GPR), decision tree (DT), least-squares boosting (LSB), bayesian ridge (BR), support vector regression (SVR), K-nearest neighbors (KNN), multilayer perceptron (MLP), and group method of data handling (GMDH) are employed to estimate DO and Chl-a concentrations. Fourth, to improve prediction accuracy, bayesian model averaging (BMA), entropy weighted (EW), and a new enhanced clustering-based hybrid technique called Entropy-ORNESS are employed to combine model outputs. The Entropy-ORNESS method incorporates a Genetic Algorithm (GA) to determine optimal weights and then combine them with EW weights. Finally, the inclusion of bootstrapping techniques introduces a stochastic assessment of model uncertainty, resulting in a robust estimator model. In the validation phase, the Entropy-ORNESS technique outperforms the independent models among the three fusion-based methods, yielding R2 values of 0.92 and 0.89 for DO and Chl-a clusters, respectively. The proposed hybrid-based methodology demonstrates reduced uncertainty compared to single data-driven models and two combination frameworks, with uncertainty levels of 0.24% and 1.16% for cluster 1 of DO and cluster 2 of Chl-a parameters. As a highlight point, the spatial analysis of DO and Chl-a concentrations exhibit similar pattern variations across varying depths of the dam according to the comparison of field measurements with the best hybrid technique, in which DO concentration values notably decrease during warmer seasons. These findings collectively underscore the potential of the upgraded weighted-based hybrid approach to provide more accurate estimations of DO and Chl-a concentrations in dynamic aquatic environments.