Reservoir Water Quality Management Research Articles

Actinobacteria produce taste and odor in drinking water reservoir: Community composition dynamics, co-occurrence and inactivation models

Taste and odor (T&O) has become a significant concern for drinking water safety. Actinobacteria are believed to produce T&O during the non-algal bloom period; however, this has not been widely investigated. In this study, the seasonal dynamics of the actinobacterial community structure and inactivation of odor-producing actinobacteria were explored. The results indicated that the diversity and community composition of actinobacteria exhibited significant spatiotemporal distribution. Network analysis and structural equation modeling showed that the actinobacterial community occupied a similar environmental niche, and the major environmental attributes exhibited spatiotemporal dynamics, which affected the actinobacterial community. Furthermore, the two genera of odorous actinobacteria were inactivated in drinking water sources using chlorine. Amycolatopsis spp. have a stronger chlorine resistance ability than Streptomyces spp., indicating that chlorine inactivates actinobacteria by first destroying cell membranes and causing the release of intracellular compounds. Finally, we integrated the observed variability in the inactivation rate of actinobacteria into an expanded Chick-Watson model to estimate its effect on inactivation. These findings will deepen our understanding of the seasonal dynamics of actinobacterial community structure in drinking water reservoirs and provide a foundation for reservoir water quality management strategies.

Water Quality Variation Law and Prediction Method of a Small Reservoir in China

Compared with the attention of large reservoirs, the water quality of small reservoirs also needs attention. In recent years, the problem of reservoir water quality has become increasingly serious. How to predict reservoir water quality may be an urgent problem to be solved. Taking the Yangmeiling reservoir as an example, this paper collects the hydrological and water quality data of the Yangmeiling reservoir in the last ten years, analyzes the relationship between hydrological and water quality data, and uses a machine learning method to simulate the relationship between water quality and hydrological data. The results show that the water quality of small reservoirs can be simply linked with hydrological data and can be predicted through hydrological data, and has high simulation accuracy. This method can be popularized in the simulation and prediction of the water quality of small reservoirs; it does not provide a theoretical basis for the water quality management of small reservoirs.

Water level–driven agricultural nonpoint source pollution dominated the ammonia variation in China's second largest reservoir

Rainfall-runoff and water flooding are the driving mechanisms of agricultural nonpoint source pollution (ANPSP), but existing research has hardly focused on water level–driven ANPSP. Danjiangkou Reservoir was the second largest reservoir in China, and its water quality was dominated by ANPSP. This study explored the effect of water level on water quality of Danjiangkou Reservoir and aimed to provide basis for water quality management of large reservoirs. The effect of water level–driven ANPSP on the concentration of reservoir ammonia was studied employing the methods of factor decomposition and multiple regression on a extensive time series data of reservoir ammonia, water level, rainfall, fertilizer usage, and inflow river ammonia. The long-term trend revealed the reservoir ammonia peaked in 2011 and the inflow river ammonia peaked in 2012 (Han River) and 2013 (Dan River), which indicated the success of point source control in the past 15 years and the dominant role of ANPSP in the reservoir ammonia in recent years. With the long-term trend series, the multiple regression results showed that 56% of the variation of the reservoir ammonia concentration was due to the water level (standardized regression coefficient 0.422), fertilizer usage (standardized regression coefficient 0.522), and inflow river ammonia (standardized regression coefficient 0.219). However, the rainfall was insignificant. The predominance of water level and fertilizer usage in explanation of the reservoir ammonia variation indicated that water level–driven ANPSP was the primary factor influencing the reservoir ammonia. The effect of water level was primarily reflected in the long-term variation of ammonia concentration rather than the seasonal variation within the year. This study showed that when compared with rainfall–driven ANPSP, water level–driven ANPSP had a greater impact on the reservoir ammonia. Water quality protection should center on the management of the water level–fluctuation zone.

Linking remote sensing analysis and reservoir operation optimization for improving water quality management of reservoirs

The present study proposes and evaluates an integrated framework in which water quality distribution model of the lake and reservoir operation optimization are linked for improving the environmental management of the reservoir. Coupled remote sensing data processing- machine learning model was utilized to simulate water quality distribution in the reservoir in which one machine-learning model was used for converting the spectral images to the total dissolved solids (TDS) distribution in the reservoir. Then, another machine-learning model was developed based on the generated data by the first model for predicting water quality distribution map for unseens reservoir inflow/outflow scenarios. This machine-learning model was applied in the structure of reservoir operation optimization. Particle swarm optimization was utilized in the optimization process. Based on the results in the case study, the proposed method is able to minimize environmental impacts in the reservoir while the benefits from the reservoir such as water supply are maximized. However, its performance is not perfect that seems logical. In fact, the proposed model is able to balance the environmental requirements and losses of the reservoir. According to the generated water quality distribution map in a critical month of the simulated period, TDS concentration alters in range of 258 mg/L to 1091 mg/L in the lake that demonstrates using water quality distribution model in the optimal reservoir operation is necessary. The proposed method highlights the importance of using the remote sensing-based models in the structure of water resources optimization systems.

Assessment of Water Quality Based on Trophic Status and Nutrients-Chlorophyll Empirical Models of Different Elevation Reservoirs

Water quality degradation is one of the most pressing environmental challenges in reservoirs around the world and makes the trophic status assessment of reservoirs essential for their restoration and sustainable use. The main aims of this study were to determine the spatial variations in water quality and trophic state of 204 South Korean reservoirs at different altitude levels. The results demonstrated mean total phosphorus (TP), chlorophyll-a (CHL-a), total suspended solids (TSS), organic matter indicators (chemical oxygen demand: COD; total organic carbon: TOC), water temperature (WT), and electrical conductivity (EC) remain consistently higher in the very lowland reservoirs (VLLR) than those in other altitudes, due to sedimentary or alluvial watersheds. The average TP and CHL-a levels in VLLR crossed the limit of the eutrophic water, symptomizing a moderate risk of cyanobacterial blooms. Empirical models were developed to identify critical variables controlling algal biomass and water clarity in reservoirs. The empirical analyses of all reservoir categories illustrated TP as a better predictor of CHL-a (R2 = 0.44, p < 0.01) than TN (R2 = 0.02, p < 0.05) as well as showed strong P-limitation based on TN:TP ratios. The algal productivity of VLLR (R2 = 0.61, p < 0.01) was limited by phosphorus, while highland reservoirs (HLR) were phosphorus (R2 = 0.23, p < 0.03) and light-limited (R2 = 0.31, p < 0.01). However, TSS showed a highly significant influence on water clarity compared to TP and algal CHL-a in all reservoirs. TP and TSS explained 47% and 34% of the variance in non-algal turbidity (NAT) in HLR. In contrast, the TP and TSS variances were 18% and 29% in midland reservoirs (MLR) and 32% and 20% in LLR. The trophic state index (TSI) of selected reservoirs varied between mesotrophic to eutrophic states as per TSI (TP), TSI (CHL-a), and TSI (SD). Mean TSI (CHL-a) indicated all reservoirs as eutrophic. Trophic state index deviation (TSID) assessment also complemented the phosphorus limitation characterized by the blue-green algae (BGA) domination in all reservoirs. Overall, reservoirs at varying altitudes reflect the multiplying impacts of anthropogenic factors on water quality, which can provide valuable insights into reservoir water quality management.

Effects of hydrological regimes and limnological parameters on plankton community properties in tropical irrigation reservoirs: A Sri Lankan case study

AbstractWater‐level fluctuations are reported to be key processes influencing ecosystem structure and functions in lentic aquatic ecosystems such as reservoirs. The plankton community structure in reservoirs, being useful monitoring tools as reservoir trophic status indicators, are influenced by water quality parameters and water‐level fluctuations. The present study tested the hypothesis that plankton community properties in irrigation reservoirs are influenced by hydrological regimes and physical and chemical parameters. Accordingly, the effects of physical, chemical and hydrological parameters on plankton density and taxa richness were investigated for 10 selected irrigation reservoirs of Sri Lanka. The relative reservoir level fluctuation (RRLF), an index of the magnitude of water‐level fluctuation in relation to mean depth, had a positive influence on phytoplankton density. Because the RRLF positively influenced phytoplankton density, this index appeared to favour eutrophy in reservoirs. Canonical correspondence analysis (CCA) indicated both the phytoplankton and zooplankton community structure in the study reservoirs was influenced by physical and chemical characteristics. CCA also indicated the plankton taxa abundance and water quality parameters could be used for the trophic classification of irrigation reservoirs. The results of the present study highlight the need for examining attributes based on hydrological regimes in the context of reservoir water quality management.

Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting

ABSTRACT Near-term, iterative ecological forecasts with quantified uncertainty have great potential for improving lake and reservoir management. For example, if managers received a forecast indicating a high likelihood of impending impairment, they could make decisions today to prevent or mitigate poor water quality in the future. Increasing the number of automated, real-time freshwater forecasts used for management requires integrating interdisciplinary expertise to develop a framework that seamlessly links data, models, and cyberinfrastructure, as well as collaborations with managers to ensure that forecasts are embedded into decision-making workflows. The goal of this study is to advance the implementation of near-term, iterative ecological forecasts for freshwater management. We first provide an overview of FLARE (Forecasting Lake And Reservoir Ecosystems), a forecasting framework we developed and applied to a drinking water reservoir to assist water quality management, as a potential open-source option for interested users. We used FLARE to develop scenario forecasts simulating different water quality interventions to inform manager decision-making. Second, we share lessons learned from our experience developing and running FLARE over 2 years to inform other forecasting projects. We specifically focus on how to develop, implement, and maintain a forecasting system used for active management. Our goal is to break down the barriers to forecasting for freshwater researchers, with the aim of improving lake and reservoir management globally.

Water quality index and spatio-temporal perspective of a large Brazilian water reservoir

Abstract The water spatio-temporal variability of the Irapé Hydroelectric Power Plant reservoir and its main tributaries was evaluated by analysing the temporal trend of the main parameters and applying the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI), using data from 2008 to 2018. This reservoir is in Minas Gerais, Brazil, and covers an area of approximately 143 km2 across seven municipalities. The dissolved iron (DFe) presented the highest percentage of standards violations (31.7% to 80.5%), most frequently in the reservoir tributaries. The Mann–Kendall tests indicated that the monitoring stations showed an increasing trend of 78.5% N–NH4+ and 64.1% DFe. During the evaluated period, the reservoir waters were classified as excellent (1.2%), good (61.3%), acceptable (29.5%), and poor (8.0%) according to the WQI for the proposed use. The poorest quality classes were more frequent in the tributaries, especially in the year 2009. The WQI seasonal assessment indicated a worsening during the rainy season in 57% of the stations, as a result of external material transport to the water bodies. The CCME WQI, in conjunction with temporal statistical analysis, contributed to the interpretation of the monitoring data, generating important information for reservoir water quality management.

Mapping the chlorophyll‐a concentrations in hypereutrophic Krishnagiri Reservoir (India) using Landsat 8 Operational Land Imager

AbstractKrishnagiri Reservoir exhibits a hypereutrophic status and continuously receives external sediment and nutrient loads, in addition to its internal phosphorus loading, both affecting the reservoir water quality. Increased nutrient loading attributable to changing anthropogenic activities in the catchment area will further exacerbate the deteriorating trophic status. Temporal Satellite imageries can play a crucial role in the rapid assessment of the trophic status of the reservoir over a large spatial extent. The eutrophication status of freshwater systems is directly related to the chlorophyll‐a (Chl‐a) concentration, which represents a major trophic state indicator by reflecting green and absorbing violet‐blue and orange‐red light of the solar energy spectrum. The present study was undertaken to map seasonal Chl‐a concentration variations using Landsat 8 Operational Land Imager (OLI) images. Multiple regression equations developed using reflectance in the Green, Near Infrared, Shortwave Infrared 1 and 2 and Coastal bands (R2 = .635) were found to be the best fit of the model in mapping the Chl‐a concentration variations in Krishnagiri Reservoir. The derived regression model also can be used to determine the trophic state of the reservoir and can facilitate a more rapid assessment for developing management strategies for sustainable reservoir water quality management.

Study on turbidity prediction method of reservoirs based on long short term memory neural network

Turbidity is one of the important indicators in water quality management of reservoir. There are many factors affecting turbidity, and its time series is non-linear, making prediction difficult. Therefore, it is necessary to carry out research on reservoir turbidity prediction methods. In this study, the Long Short-Term Memory (LSTM) neural network was identified, validated and tested for the computation of turbidity in the Qingcaosha Reservoir. The model employed historical data of turbidity, water level, wind direction and wind speed over a period of 2 years at various monitoring points. Within 40 iterations of the model, the mean square error converged to less than 0.05 steadily, and the Nash efficiency coefficient of the 24 h prediction was above 0.5. It showed that the model has the characteristics of fast convergence, high stability, and accurate prediction, which meant this model can be well applied to prediction of reservoir turbidity. This study also tried to use the forecasted wind field data to improve the actual turbidity prediction of the reservoir. The results showed that the accuracy is slightly lower than the predicted result using the measured wind field data, but it was significantly higher than the prediction result using the extended wind field data at the previous time point. Therefore, using forecasted wind field data can effectively improve the accuracy of the actual reservoir turbidity forecast. The results of this study indicate that the LSTM neural network model is fast, stable, and highly accurate, indicating that it is suitable for prediction of turbidity in reservoirs and can provide support for water quality management of reservoirs.

Characterizing Density Flow Regimes of Three Rivers with Different Physicochemical Properties in a Run-Of-The-River Reservoir

Inflow mixing affects the spatiotemporal heterogeneity of water quality in reservoirs. Reservoir water quality management requires accurate prediction of density flow regimes to understand the spatiotemporal distribution of dissolved and particulate nutrients and organics. This study aims to characterize the mixing and circulation of three rivers with different physicochemical properties in a run-of-the-river (ROR) reservoir, using high-frequency monitoring and three-dimensional (3D) hydrodynamic modeling. The Aquatic Ecosystem Model (AEM3D) was constructed for the reservoir and calibrated with high-frequency data obtained from May–June 2016, accurately reproducing the observed spatiotemporal variations of flow velocity, water temperature, and electrical conductivity (EC) in the reservoir. High-frequency data and 3D model results showed that mixing of the rivers in the ROR reservoir is governed by density flow regimes formed by influent water temperature differences. At the confluence, colder and warmer river influents formed underflows and surface buoyant overflows, respectively. The spatial arrangement of flow direction, water residence time, and EC concentration were largely controlled by the buoyancy-driven flow. Stagnant areas with long residence times corresponded with areas of observed algal blooms and hypoxia. High-frequency sensor technology, combined with 3D hydrodynamic modeling, is effective for understanding the complex flow regimes and associated water quality characteristics in ROR-type reservoirs.

An Adaptive Surrogate Assisted CE-QUAL-W2 Model Embedded in Hybrid NSGA-II_ AMOSA Algorithm for Reservoir Water Quality and Quantity Management

The Meimeh dam construction is a project, planned to provide sustainable livelihoods, social, and economic developments in the Meimeh River Basin, Ilam, Iran. However, due to the high concentration of TDS in the Meimeh River and its tributaries, river impoundment and water storage can be harmful to the Meimeh Reservoir. The upstream inflow control and the reservoir operation management in a selective withdrawal scheme (SWS) were used to mitigate the potential environmental degradation of Meimeh Reservoir’s low water quality. CE-QUAL-W2 and WEAP (Water Evaluation and Assessment Programming) models were employed to evaluate the effects of various upstream saline inflow control scenarios. The analysis indicated that the diversion of the Siyoul tributary flow rate in the Meimeh River could result in lower violations of Total Dissolved Solids (TDS) concentrations and better water supply satisfaction. Then, the optimal reservoir operation management strategies in a SWS were derived in the best upstream inflow control scenario. The adaptive surrogate-assisted WQSM (water quality simulation model), coupled to hybrid NSGA-II_AMOSA (Non-dominated Sorting Genetic Algorithm-II_Archived Multi-Objective Simulated Annealing) algorithm, has been applied to derive the suitable reservoir operation strategies in SWS, improve the water supply satisfaction and alleviate the adverse effects of reservoir outflow TDS violations. The performances of the best water quality and supply scenarios have been compared with the scenario based on the standard operation policy (SOP) in the Meimeh Reservoir. The results show that most violations of TDS criteria occur during the peak agricultural seasons and significant water deficit in some dry years happens in the SOP.

Effect of local watershed landscapes on the nitrogen and phosphorus concentrations in the waterbodies of reservoir bays.

Reservoir bays, which are affected by the reservoir and watershed characteristics, are the initial and most sensitive areas in the evolution process of reservoir water quality. However, the relationship between the watershed characteristics and nitrogen and phosphorus concentrations in reservoir bays is poorly understood. We selected 66 bays from the Danjiangkou Reservoir and sampled twice per year (storage and discharge periods) from 2015 to 2018 to monitor the total nitrogen (TN) and total phosphorus (TP) concentration in the waterbodies of the reservoir bays. Four types of watershed characteristic indices (topographic variables, soil variables, land-use composition, and landscape patterns) around these bays were obtained. We quantified the relationship between the TN and TP concentrations and watershed characteristics in the waterbodies of the reservoir bays using partial least squares regression (PLSR). The results showed that the mean concentrations of TN and TP in the storage period (TN:1.69mg·L-1, TP:0.088mg·L-1) were higher than those in the discharge period (TN:1.22mg·L-1, TP:0.063mg·L-1). The optimal PLSR models explained 67.9% and 82.5% of the TN concentration variability, and 65.4% and 67.2% of the TP concentration variability during the storage and discharge period, respectively. Based on the variable importance in the projection (VIP) values, soil erodibility had significant effects on the TN and TP concentrations. The key factors affecting the TN concentration were the slope gradient, basin relief, topographic wetness index, forest and agricultural land use, whereas the factors controlling the TP concentration were the landscape shape index, edge density, Shannon's diversity index and grass land use, although the TP concentration was also controlled by the patch density and contagion during the storage period, and by mean patch size and largest patch index during the discharge period. This study provides critical insights into sustainable landscape planning and effective reservoir water quality management.

Influence of land use and rainfall on the optical properties of dissolved organic matter in a key drinking water reservoir in China

The concentration, source and composition of dissolved organic matter (DOM) in aquatic ecosystems are associated with land use and hydrological connectivity between terrestrial and aquatic systems. However, direct evidence of the effects of rainfall and land use on the variability of DOM in aquatic ecosystems is very limited. In this study, chromophoric DOM (CDOM) absorption and fluorescence spectroscopy were used to elucidate how rainfall and land use affect the variability of CDOM in the watershed of Lake Tianmu, a key drinking water reservoir in the Yangtze River Delta. The mean values of the fluorescence intensity (Fmax) of parallel factor analysis-derived humic-like components (C1, C3, C6) and tryptophan-like components C5 were higher in the southeastern inflowing river mouths than those downstream of the lake outlet regions. The upstream tributaries were mainly dominated by humic-like materials, while the lake was mainly dominated by protein-like materials. The Fmax values of four humic-like components and two tryptophan-like components all increased significantly as the %woodland decreased, but %anthropogenic land use (%cropland+%urban construction area) increased. The Fmax of the humic-like components at the inflowing tributaries and the lake increased with increasing rainfall during storm events, and the value was especially pronounced at the inflowing river mouths. We concluded that land use and hydrological conditions play an important role in influencing the CDOM source and optical composition, and these findings provide insights for the understanding of aquatic ecosystem metabolism and reservoir water quality management.

Using Exceedance Probability to Determine Total Maximum Daily Loads for Reservoir Water Quality Management

Total maximum daily loads (TMDLs) are used to protect water bodies based on their assimilative ability and are transferred as a maximum allowable load, which is the sum of all pollution emissions in a watershed that cannot be exceeded. This allowable load is calculated from a target water quality concentration and a flow state. The target water quality concentration is typically consistent with water quality standards; however, it is difficult to determine which flow state to use, especially for lakes and reservoirs. In this study, an exceedance probability method is established for determining the TMDL for reservoirs. The SWMM (Storm Water Management Model) was used to understand the pollution loads from the watershed, and the Vollenweider model was used to simulate the total phosphorous (TP) concentrations in the reservoir. Using the validated Vollenweider model, the relationship between pollution loads and the target TP concentration is illustrated. This relationship is associated with real changes in the reservoir water volume and is presented as the exceedance probability. In the study area (i.e., the Shiman Reservoir in Taiwan), an exceedance probability of 50% is suggested, and the allowable TP load is 22,209 kg/year when considering a target TP concentration of 20 μg/L. When considering effective management, the pollution sources in three hot spots are priorities, and controlling their point and nonpoint pollution sources can decrease TP from 25 to 22 μg/L.

Integrated Modelling System with Uncertainty Analysis for Reservoir Water Quality Management in a Reclamation River Basin

AbstractA developed integrated modelling system with uncertainty analysis (Monte Carlo simulation and generalized likelihood uncertainty estimation) was used to evaluate the effect of uncertainty sources on long‐term water quality and develop river basin management measures to meet the specified water quality criteria based on the predicted probability of occurrence in the reclamation river basin. The results of the deterministic integrated modelling system without uncertainty analysis showed that the Hwaong Reservoir water quality for total phosphorus (T‐P) (0.094 mg l−1) in 2022 would meet the reservoir water quality standard (0.1 mg l−1). However, the water quality prediction for the Hwaong Reservoir in 2022 using the integrated modelling system with uncertainty analysis showed that only 28% would meet the T‐P water quality standard, indicating that to meet the water quality standard with a 90% level of confidence, further river basin management measures should be applied in addition to the construction of planned wastewater treatment plants and treatment wetlands. The developed integrated modelling system with uncertainty analysis was demonstrated to be advantageous because it can allow modellers to make useful decisions about whether a river basin management plan can meet specified water quality criteria based on the probability of occurrence. Copyright © 2016 John Wiley & Sons, Ltd.

Developing statistical models for estimating chlorophyll-a and total suspended solid levels at an estuarine reservoir with nutrient inputs from satellite observations

Providing high-resolution monitoring data is essential in promoting decision-making activities for surface water quality protection such as scientific modeling and data analysis. In this study, statistical models using multiple linear regression (MLR) were developed to estimate Chlorophyll-a (Chl-a) and total suspended solid (TSS) concentrations in a mesotrophic reservoir, the Yeongam Reservoir in Korea, from satellite observations. Two types of satellite data that covered different spectral regions, for wavelengths in 412–865nm (for the Geostationary Ocean Color Imager) and those in 405–14,385nm (for the Moderate Resolution Imaging Spectroradiometer), were used as inputs for statistical models, after bias correction. The MLR models for Chl-a and TSS were initially constructed and evaluated with 39 image data-sets obtained during 2011–2014. Subsequently, they compared with their corresponding algorithms that were developed under different environmental settings as well as the CE-QUAL-W2 model, a numerical model of reservoir water quality. Sensitivity analysis showed that specific red and near-infrared wavelengths significantly contributed to improve the accuracy of the Chl-a and TSS estimates, respectively, along with those of blue and green bands typically used. The constructed MLR models showed better performance than the simulation model as well as the classical and recent bio-optical algorithms on average. In particular, poor prediction performance for total nitrogen and total phosphorus caused by a lack of adequate input data and description of transport mechanisms appeared to lower the accuracy of the Chl-a and TSS estimates in the simulation model. Therefore, these results demonstrate that statistical models developed from satellite observations can be used to rapidly screen local water quality and to provide high-quality data for reservoir water quality management.

Investigation on trophic state index by artificial neural networks (case study: Dez Dam of Iran)

Dam construction and surface runoff control is one of the most common approaches for water-needs supply of human societies. However, the increasing development of social activities and hence the subsequent increase in environmental pollutants leads to deterioration of water quality in dam reservoirs and eutrophication process could be intensified. So, the water quality of reservoirs is now one of the key factors in operation and water quality management of reservoirs. Hence, maintaining the quality of the stored water and identification and examination of changes along time has been a constant concern of humans that involves the water authorities. Traditionally, empirical trophic state indices of dam reservoirs often defined based on changes in concentration of effective factors (nutrients) and its consequences (increase in chlorophyll a), have been used as an efficient tool in the definition of dam reservoirs quality. In recent years, modeling techniques such as artificial neural networks have enhanced the prediction capability and the accuracy of these studies. In this study, artificial neural networks have been applied to analyze eutrophication process in the Dez Dam reservoir in Iran. In this paper, feed forward neural network with one input layer, one hidden layer and one output layer was applied using MATLAB neural network toolbox for trophic state index (TSI) analysis in the Dez Dam reservoir. The input data of this network are effective parameters in the eutrophication: nitrogen cycle parameters and phosphorous cycle parameters and parameters that will be changed by eutrophication: Chl a, SD, DO and the output data is TSI. Based on the results from estimation of modified Carlson trophic state index, Dez Dam reservoir is considered to be eutrophic in the early July to mid-November and would be mesotrophic with decrease in temperature. Therefore, a decrease in water quality of the dam reservoir during the warm seasons is expectable. The results indicated that artificial neural network (ANN) is a suitable tool for quality modeling of reservoir of dam and increment and decrement of nutrients in trend of eutrophication. Therefore, ANN is a suitable tool for quality modeling of reservoir of dam.

A simulation-optimization approach for assessing optimal wastewater load allocation schemes in the Three Gorges Reservoir, China

Abstract Background The Three Gorges Reservoir (TGR) has been facing deteriorated water quality issues since the construction of the Three Gorges Dam (TGD) in 1994. However, no previous studies have used a simulation-optimization assessment framework to examine the waste-load allocation patterns in the TGR area for alleviating its water pollution problem. In this study, a simulation-optimization modeling approach was developed for addressing this issue, through combining an environmental fluid dynamic code (EFDC)-based water quality simulation model and a waste-load allocation optimization model into a general framework. Results The approach was applied to a TGR section (Changshou-Fuling section) for identifying the optimal waste-load allocation schemes among its 11 wastewater discharge outlets. Firstly, the EFDC model was run to simulate the water quality response in the receiving water body under a single discharge load scenario, and the simulated COD and NH4 +-N concentrations were used to calculate the pollution mixing zone (PMZ), the pollution mixing zone per unit load (PMZPL), and sensitivity index (SI) pertaining to that outlet. These values were then used in the formulation of the waste-load allocation optimization model, with its objective being to maximize the environmental performance under constraints that existing waste discharge loads in terms of total wastewater amount, total pollutant mass, and existing PMZ size can’t be exceeded. Conclusions Modeling results give an optimal waste-load allocation ratio for each discharge outlet within the study section, and its implications to the reservoir water quality management were analyzed. It is anticipated that the develop approach can be extended to the entire TGR area for better water quality management studies and practices.

Artificial neural network modeling of dissolved oxygen in reservoir

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.