Reservoir Water Quality Monitoring Research Articles

Optimization of Dam Operation and Interaction with Groundwater: An Overview Focusing on Greece

The optimization of dam operations to transform them into multi-objective facilities constitutes a challenge for both hydrology, hydrogeology, and hydropower generation. However, the use of the optimal algorithm for such transformation is critically important. Additionally, the literature has highlighted that dams might negatively influence the recharge of groundwater. Within this study, we provide an overview of the available algorithms for the optimization of dam operations. Additionally, an overview focusing on hydropower generation in Greece illustrates the high potential of the Mediterranean region for hydropower generation and the application of MAR. The water quality of the reservoirs is also highlighted as a critical parameter. Within this study, we present indices for water quality monitoring in dam reservoirs, while the most prevailing index is the SRDD. This study constitutes a guide for researchers in choosing the optimal tools for the optimization of dam operations and the water quality monitoring of reservoirs. The present study suggests a meta-heuristic optimization methodology using the harmony search algorithm. The model uses a geometric model of the reservoir and calculates the level–supply curve. Furthermore, a multi-criteria optimization model was developed with two objective functions: the maximum power output from the hydroelectric power plant turbines and the optimal groundwater recharge. The model with appropriate parameter modifications can be applied to any small dam as it is a decision- and policy-making methodology, independent of local conditions. A further step is the application of these approaches dealing with field data and the numerical modeling of case studies. The interdisciplinary approach of this study links deferent aspect and scientific perceptions, providing a comprehensive guide to optimal water resource management and environmental sustainability.

Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies.

Water quality monitoring of reservoirs is currently a significant challenge in the tropical regions of the world due to limited monitoring stations and hydrological data. Remote sensing techniques have proven to be a powerful tool for continuous real-time monitoring and assessment of tropical reservoirs water quality. Although many studies have detected chlorophyll-a (Chl-a) concentrations as a proxy to represent nutrient contamination, using Sentinel 2 for eutrophic or hypereutrophic inland water bodies, mainly reservoirs, minimal efforts have been made for oligotrophic and mesotrophic reservoirs. The present study aimed to develop a modeling framework to map and estimate spatio-temporal variability of Chl-a levels and associated water spread using the Modified Normalized Difference Water Index (MNDWI) and Maximum Chlorophyll Index (MCI). Moreover, the impact of land use/land cover type of the contributing watershed in the oligo-mesotrophic reservoir, Bhadra (tropical reservoir), for 2018 and 2019 using Sentinel 2 satellite data was analyzed. The results show that the water spread area was higher in the post-monsoon months and lower in the summer months. This was further validated by the correlation with reservoir storage, which showed a strong relationship (R2 = 0.97, 2018; R2 = 0.93, 2019). The estimated Chl-a spread was higher in the winter season, because the reservoir catchment was dominated by deciduous forest, producing a large amount of leaf litter in tropical regions, which leads to an increase in the level of Chl-a. It was found that Chl-a spread in the reservoir, specifically at the inlet sources and near agricultural land practices (western parts of the Bhadra reservoir). Based on the findings of this study, the MCI spectral index derived from Sentinel 2 data can be used to accurately map the spread of Chl-a in diverse water bodies, thereby offering a robust scientific basis for effective reservoir management.

Single and mixed exposure to distinct groups of pesticides suggests endocrine disrupting properties of imidacloprid in zebrafish embryos

Due to their selective toxicity to insects, nicotinoid compounds have been widely used to control pests in crops and livestock around the world. However, despite the advantages presented, much has been discussed about their harmful effects on exposed organisms, either directly or indirectly, with regards to endocrine disruption. This study aimed to evaluate the lethal and sublethal effects of imidacloprid (IMD) and abamectin (ABA) formulations, separately and combined, on zebrafish (Danio rerio) embryos at different developmental stages. For this, Fish Embryo Toxicity (FET) tests were carried out, exposing two hours post-fertilization (hpf) zebrafish to 96 hours of treatments with five different concentrations of abamectin (0.5–11.7 mg L−1), imidacloprid (0.0001–1.0 mg L−1), and imidacloprid/abamectin mixtures (LC50/2 − LC50/1000). The results showed that IMD and ABA caused toxic effects in zebrafish embryos. Significant effects were observed regarding egg coagulation, pericardial edema, and lack of larvae hatching. However, unlike ABA, the IMD dose-response curve for mortality had a bell curve display, where medium doses caused more mortality than higher and lower doses. These data demonstrate the toxic influence of sublethal IMD and ABA concentrations on zebrafish, suggesting that these compounds should be listed for river and reservoir water-quality monitoring.

Machine learning techniques in river water quality modelling: a research travelogue

Abstract Water is a prime necessity for the survival and sustenance of all living beings. Over the past few years, the water quality of rivers has been adversely affected due to harmful wastes and pollutants. This ever-increasing water pollution is a matter of great concern as it is deteriorating the water quality, making it unfit for any type of use. Contaminated water resources can cause serious effects on humans as well as aquatic life. Hence, water quality monitoring of reservoirs is essential. Recently, water quality modelling using AI techniques has generated a lot of interest and it can be very beneficial in ecological and water resources management. This paper presents the state-of-the-art application of machine learning techniques in forecasting river water quality. It highlights the different key techniques, advantages, disadvantages, and applications with respect to monitoring the river water quality. The review also intends to find the existing challenges and opportunities for future research.

Handling uncertainty in optimal design of reservoir water quality monitoring systems

In the present paper, a scenario-based many-objective optimization model is developed for the spatio-temporal optimal design of reservoir water quality monitoring systems considering uncertainties. The proposed methodology is based on the concept of nonlinear interval number programming and information theory, while handling uncertainties of temperature, reservoir inflow, and inflow constituent concentration. A reference-point-based non-dominated sorting genetic algorithm (NSGA-III) is used to deal with the many-objective optimization problem. The proposed model is developed for the Karkheh reservoir system in Iran as a real-world problem. The results show excellent performance of the optimized water quality sampling locations instead of all potential ones in providing adequate information about the reservoir water quality status. The presented uncertainty-based model leads to a 55.73% reduction in the radius of the uncertain interval caused by different scenarios. Handling uncertainties in a spatio-temporal many-objective optimization problem is the main contribution of this study, yielding a reliable and robust design of a reservoir monitoring system that is less sensitive to various scenarios.

An Entropy-Based Approach to Fuzzy Multi-objective Optimization of Reservoir Water Quality Monitoring Networks Considering Uncertainties

In this study, a new fuzzy methodology for a multi-objective optimization of reservoir Water Quality Monitoring Stations (WQMS) was developed, based on Transinformation Entropy (TE), the IRanian Water Quality Index (IRWQI), and fuzzy social choice considering uncertainties. The approach was utilized in the Karkheh Dam reservoir in Iran. The objective functions were: 1) minimizing costs, 2) minimizing redundant information and uncertainties, and 3) maximizing the spatial coverage of the network. A CE-QUAL-W2 model was used for the simulation of water quality variables. The IRWQI was computed to reveal a complete picture of the reservoir water quality. The TE quantities were calculated for each pair of potential stations. The TE values were plotted against the spatial distances among potential WQMS to obtain the TE–Distance (TE–D) curve, and minimize redundant information among stations, while providing coverage of the entire network. A multi-objective Genetic Algorithm (NSGA-II) was applied to obtain Pareto-optimal solutions taking stakeholder preference into account. The most preferred solution was then obtained using fuzzy social choice approaches to achieve a consensus. The fuzziness embedded in the decision-making procedure, the uncertainty in the value of mutual information, and the uncertainty in identifying the optimal distance among WQMS were also investigated. Results indicated that the three fuzzy social choice approaches (Borda Count, Minimax, and Approval Voting) led to the same number of optimized WQMS in each fuzzy alpha-cut. Based on the fuzzy linguistic quantifiers method, the number of optimized WQMS was increased.

Influence of Extreme Strength in Water Quality of the Jucazinho Reservoir, Northeastern Brazil, PE

The Jucazinho reservoir was built in the State of Pernambuco, Northeastern Brazil, to water supply in a great part of the population that live in the semi-arid of Pernambuco. This reservoir controls the high part of Capibaribe river basin, area affected several actions that can compromise the reservoir water quality such as disposal of domestic sewage, industrial wastewater and agriculture with use of fertilizers. This study aimed to identify the factors that lead to water quality of the Jucazinho reservoir using a database containing information of nine years of reservoir water quality monitoring in line with a multivariate statistical technique known as Principal Component Analysis (PCA). To use this technique, it was selected two components which determine the quality of the reservoir water. The first principal component, ranging from an annual basis, explained the relationship between the development of cyanobacteria, the concentration of dissolved solids and electrical conductivity, comparing it with the variation in the dam volume, total phosphorus levels and turbidity. The second principal component, ranging from a mensal basis, explained the photosynthetic activity performed by cyanobacteria confronting with the variation in the dam volume. It observed the relationship between water quality parameters with rainfall, featuring an annual and seasonal pattern that can be used as reference to behaviour studies of this reservoir.

Stakeholder engagement in multi-objective optimization of water quality monitoring network, case study: Karkheh Dam reservoir

Reservoir water quality is important for water quality management downstream. A hierarchical approach is developed to present the monitoring locations within a format that satisfies the objectives of social stakeholders for making final decisions. First, a CE-QUAL-W2 model is applied to simulate water quality variables in the reservoir for a long time using a set of historic data. Second, transinformation entropy theory is used to quantify mutual information among a set of monitoring stations for each water quality variable. Then, a non-dominating sorting genetic algorithm-based model is developed for multi-objective optimization of the water quality monitoring network. Finally, a social choice method is applied to the identified non-dominated solutions to achieve a strategy that is compromised among stakeholders. The variations of the water quality variables at different depths and different seasons are investigated. The proposed approach is illustrated for Karkheh Reservoir in Iran. The number of optimized monitoring stations is the same for all seasons (three out of 22 potential stations) using different social choice methods. The results show the appropriate performance of the proposed methodology for optimization of reservoir water quality monitoring stations.

Predictive Analysis of Water Quality Parameters using Deep Learning

and reservoirs are important water resources. Reservoirs are vital water resources to support all living organism. They provide clean water and habitat for a complex variety of aquatic life. Water from such resources can be used for diverse purposes such as, industry usage, agriculture and supplies for drinking water and recreation and aesthetic value. Apart from this, reservoirs also helpful to get hydro-electric power, flood control and scenic beauty. Water collected in such resources can be utilized in drought situation also. Unfortunately, these important resources are being polluted and the quality of water is being influenced by numerous factors. The quality of water is deteriorated by anthropogenic activities, indiscriminate disposal of sewage, human activities and also industry waste. Water quality monitoring of reservoirs is essential in exploitation of aquatic resources conservation. The quality of water helps in regulating the biotic diversity and biomass, energy and rate of succession. Moreover, contaminated water can lead to some waterborne diseases and also influences child mortality. In order to reduce effect of contaminated water, it is essential to assess different aspects of water quality. Predicting water quality parameters a few steps ahead can be beneficial to achieve this. The main objective of this study is to provide fairly accurate predictions for variable data. The research was carried out by using the secondary data collected from a third party for Chaskaman River located near Nasik, Maharashtra, India on WEKA tool. The study shows that deep learning techniques which use unsupervised learning to provide accurate results as compared to the techniques based on supervised learning. The comparison of results show that robustness can be achieve by denoising autoencoder and deep belief network and also successfully handle the variability in the data. Merit of the unsupervised learning algorithms are evaluated on the basis of metrics such as mean absolute error and mean square error to examine the error rate of prediction.

Application of Multispectral Sensors Carried on Unmanned Aerial Vehicle (UAV) to Trophic State Mapping of Small Reservoirs: A Case Study of Tain-Pu Reservoir in Kinmen, Taiwan

Multispectral, as well as multi-temporal, satellite images, coupled with measurements, in situ, have been widely applied to the water quality monitoring of reservoirs. However, the spatial resolutions of the current multispectral satellite imageries are inadequate for trophic state mapping of small reservoirs which merely cover several hectares. Moreover, the temporal gap between effective satellite imaging and measurements, in situ, is usually a few days or weeks; this time lag hampers the establishment of regression models between band ratios and water quality parameters. In this research, the RGB and NIR sensors carried on an unmanned aerial vehicle (UAV) were applied to the trophic state mapping of Tain-Pu reservoir, which is one of the small reservoirs in Kinmen, Taiwan. Due to the limited sampling points and the uncertainty of water fluidity, the average method and the matching pixel-by-pixel (MPP) method were employed to search for the optimal regression models. The experimental results indicate that the MPP method can lead to better regression models than the average method, and the trophic state maps show that the averages of Chl-a, TP, and SD are 179.7 μg·L−1, 108.4 μg·L−1, and 1.4 m, respectively.

Reservoir water quality monitoring using remote sensing with seasonal models: case study of five central-Utah reservoirs

Remote sensing models estimate chlorophyll concentrations by correlating spectral reflectance and reservoir chlorophyll. Different algal populations have different spectral signatures and thus different correlation models, an issue typically addressed by developing and applying a model using the same satellite image. Here we exploit these population differences by developing seasonal models that can be applied to other images from that season. We rely on algal succession and assume the phytoplankton population is relatively constant over a season, dividing the growth season into 3 parts as substitutes for population measurements. We present seasonal models developed using data from 2 Utah reservoirs, Deer Creek and Jordanelle, which have comprehensive long-term field datasets large enough to provide adequate near-coincident data for model development. We then apply the chlorophyll-estimation models to 5 reservoirs in north-central Utah and present the trends in the average, maximum, and variance of the chlorophyll concentration for each reservoir over a nearly 40-year period. We present examples of chlorophyll distribution maps that show spatial patterns and discuss implications for field sampling design and analysis. We found that season-specific models perform well for satellite images from the same season but do not perform well against images from other seasons. We suggest that these models can be used with confidence in the season for which they were developed, allowing analysis of historical data and providing current information on reservoir conditions without accompanying field samples.

Effects of oxygen and nitrate on nutrient release from profundal sediments of a large, oligo-mesotrophic reservoir, Lake Mathews, California

Lake Mathews is a large, oligo-mesotrophic reservoir located in Southern California. The reservoir has elevated levels of nitrate and periodically experiences hypolimnetic anoxia. Experimental sediment-water chamber incubations and reservoir water quality monitoring were conducted to evaluate how oxygen and nitrate in overlaying water affect nutrient release from profundal sediments and internal nutrient loading. In experimental incubations, under nitrate-free anoxic conditions, sediment nutrient release rates were 3.4 ± 0.8 mg-P/m2 ·d for phosphate and 2.8 ± 1.2 mg-N/m2 ·d for ammonia (average ± standard deviation; n = 6). Oxygen repressed phosphate release and greatly diminished ammonia release from sediments in experimental incubations while nitrate only repressed phosphate release. Similar nutrient release dynamics were observed in the reservoir. Nutrient release rates estimated from seasonal nutrient profiles collected from the reservoir were 3.4 mg-P/m2 ·d for phosphate and 2.5 mg-N/m2 ·d for ammonia. Ammonia accumulation in the hypolimnion commenced with the onset of anoxic conditions, but phosphate accumulation did not start until nitrate disappeared from bottom waters approximately 6 weeks later. The time lag decreased total internal phosphorus loading by approximately 25% relative to hypothetical nitrate-free conditions. Laboratory and field data show that both oxygen and nitrate repress sediment phosphate release, likely via the maintenance of an oxidized surficial sediment layer that retains phosphate in iron-oxide complexes. However, only oxygen and not nitrate was effective in decreasing sediment ammonia release, likely by enhancing biological nitrification and assimilation in surficial sediments under oxic conditions. A number of in-lake management strategies have been developed to inhibit internal nutrient loading including calcium nitrate addition, aluminum sulfate addition, and oxygenation. In our view, the deliberate addition of nitrate to lakes and reservoirs poses several risks that must be carefully considered when evaluating strategies to control sediment phosphorus release.

Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery

Parallel GEGA was constructed by incorporating grammatical evolution (GE) into the parallel genetic algorithm (GA) to improve reservoir water quality monitoring based on remote sensing images. A cruise was conducted to ground-truth chlorophyll- a (Chl- a) concentration longitudinally along the Feitsui Reservoir, the primary water supply for Taipei City in Taiwan. Empirical functions with multiple spectral parameters from the Landsat 7 Enhanced Thematic Mapper (ETM+) data were constructed. The GE, an evolutionary automatic programming type system, automatically discovers complex nonlinear mathematical relationships among observed Chl- a concentrations and remote-sensed imageries. A GA was used afterward with GE to optimize the appropriate function type. Various parallel subpopulations were processed to enhance search efficiency during the optimization procedure with GA. Compared with a traditional linear multiple regression (LMR), the performance of parallel GEGA was found to be better than that of the traditional LMR model with lower estimating errors.

Algae in the motion: Spatial distribution of phytoplankton in thermally stratified reservoirs

Phytoplankton spatial distribution patterns in four Andalusian reservoirs with different physical characteristics are described and evaluated in this work. Both vertical and horizontal distribution patterns are presented in order to demonstrate that thermally stratified reservoirs are dynamic, complex and heterogeneus ecosystems. Vertically, phytoplankton patchiness was physically controlled by turbulent mixing and light climate and biologically determined by the hydromechanical characteristics of each microalgae functional group. The horizontal distribution of phytoplankton was the result of the interaction between the wind-induced advective transport of water masses and the vertical distribution of each algal group. The precise knowledge of the phytoplankton spatial distribution patterns and their responsible agents constitutes a powerful tool for a limnologicallybased dynamic reservoir management and must be integrated in reservoir water quality monitoring procedures. The use of fastresponse and high-resolution technologies, such as in vivo and in situ spectrofluorimetry, should significantly helps in reaching this objective.