Water Quality Simulation Model Research Articles

유역유출 및 수질모의에 관한 SWMM의 자동 보정 모듈 개발

SWMM은 홍수유출 해석, 유역유출 연속모의, 수질모의가 가능한 모형으로서 전세계적으로 널리 사용되고 있는 모형이다. 하지만 유역유출 연속모의와 수질모의에는 다수의 불명확한 매개변수가 포함되어 있으므로 이는 SWMM의 사용에 제약이 되고 있다. 본 연구의 목적은 SWMM을 이용한 유역유출 연속모의와 수질모의의 정확도를 높이고 효율성을 향상시킬 수 있도록 자동 보정 모듈을 개발하는 것이다. SWMM의 자동 보정 모듈은 전역최적화 알고리즘인 집합체 혼합진화 알고리즘과 SWMM을 연계하고, SWMM 내 추정대상 매개변수의 선정 및 적절한 탐색 범위를 설정함으로써 개발되었다. 개발된 자동 보정 모듈의 적절성은 동향 수위관측소 유역에 대하여 구성된 유역유출 및 수질모의 모형의 보정 및 검증을 통해 검토되었다. 그 결과, 자동 보정 모듈을 통해 보정된 모형은 유역의 유출현상을 매우 잘 모사하였고, 수질의 경우에도 비교적 양호한 결과를 도출하였다. 개발된 자동 보정 모듈은 향후 유역유출 모의와 수질해석에 관한 다양한 연구와 설계 등에 활용될 수 있을 것이다. The SWMM (storm water management model) has been widely used in the world and is a watershed runoff simulation model used for a single event or a continuous simulation of runoff quantity and quality. However, there are many uncertain parameters in the watershed runoff continuous simulation module and the water quality module, which make it difficult to use the SWMM. The purpose of the study is to develop an automatic calibration module of the SWMM not only for watershed runoff continuous simulation, but also water quality simulation. The automatic calibration module was developed by linking the SWMM with the SCE-UA (shuffled complex evolution-University of Arizona) that is a global optimization algorithm. Estimation parameters of the SWMM were selected and search ranges of them were reasonably configured. The module was validated by calibration and verification of the watershed runoff continuous simulation model and the water quality model for the Donghyang Stage Station Basin. The calibration results for watershed runoff continuous simulation model were excellent and those for water quality simulation model were generally satisfactory. The module could be used in various studies and designs for watershed runoff and water quality analyses.

Modeling and Field Work to Investigate the Relationship between Age and Quality of Tap Water

It has been widely theorized that water age may be a useful indicator of the quality of water within drinking water distribution networks. However, there is limited evidence of model simulation results being related to empirical water quality (WQ) data to substantiate the theory. This paper presents the findings of investigations designed to determine if there was an observable relationship between mean water ages calculated using a WQ simulation model, and measured WQ in two live distribution networks. The age of water in all pipes was calculated using Aquis hydraulic and WQ modeling software. Historic regulatory WQ data was examined to determine if there was a relationship between general WQ and calculated water age within the networks. A more detailed study was then undertaken in one network by translating model locations that were representative of the spread of water age into real-world locations. WQ samples were taken intensively from these sites and analyzed for a range of aesthetic, physical, chemical, and bacteriological parameters indicative of general WQ. Some relationship between the calculated mean age of water and the general WQ in the network was demonstrated. Analysis that considered calculated water age and associated WQ along unperturbed flow routes through the network produced a stronger relationship. Given that regulatory WQ within the study networks as reported to the Drinking Water Inspectorate (DWI) was 99.9 percent compliant, this relationship was deemed noteworthy.

Development of an inexact two-stage stochastic model with downside risk control for water quality management and decision analysis under uncertainty

Water quality management along rivers involves making water-allocation plans, establishing water quality goals, and controlling pollutant discharges, which is complicated itself but further challenged by existence of uncertainties. In this study, an inexact two-stage stochastic downside risk-aversion programming (ITSDP) model is developed for supporting regional water resources allocation and water quality management problems under uncertainties. The ITSDP method is a hybrid of interval-parameter programming, two-stage stochastic programming, and downside risk measure to tackle uncertainties described in terms of interval values and probability distributions. A water quality simulation model was provided for reflecting the relationship between the water resources allocation, wastewater discharge, and environmental responses. The proposed approach was applied to a hypothetical case for a shared stream water quality management with one municipal, three industrial and two agricultural sectors. A number of scenarios corresponding to different river inflows and risk levels were examined. The results demonstrated that the model could effectively communicate the interval-format and random uncertainties, and risk-aversion into optimization process, and generate a trade-off between the system economy and stability. They could be helpful for seeking cost-effective management strategies under uncertainties, and gaining an in-depth insight into the water quality management system characteristics, and make cost-effective decisions.

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.

Optimization of Water Distribution Systems Using Online Retrained Metamodels

AbstractThis paper proposes the use of online retrained metamodels for the optimization of water distribution system (WDS) design. In these metamodels, artificial neural networks (ANNs) are used to replace the full hydraulic and water quality simulation models and differential evolution (DE) is utilized to carry out the optimization. The ANNs in the proposed online DE-ANN model are retrained periodically during the optimization in order to improve their approximation to the appropriate portion of the search space. In addition, a local search strategy is used to further polish the final solution obtained by the online DE-ANN model. Three case studies are used to verify the effectiveness of the proposed online retrained DE-ANN model for which both hydraulic and water quality constraints are considered. In order to enable a performance comparison, a model in which a DE is combined with a full hydraulic and water quality simulation model (DE-EPANET2.0) and an offline DE-ANN model (ANNs are trained only once a...

Watershed model parameter estimation and uncertainty in data-limited environments

Parameter uncertainty and sensitivity for a watershed-scale simulation model in Portugal were explored to identify the most critical model parameters in terms of model calibration and prediction. The research is intended to help provide guidance regarding allocation of limited data collection and model parameterization resources for modelers working in any data and resource limited environment. The watershed-scale hydrology and water quality simulation model, Hydrologic Simulation Program – FORTRAN (HSPF), was used to predict the hydrology of Lis River basin in Portugal. The model was calibrated for a 5-year period 1985–1989 and validated for a 4-year period 2003–2006. Agreement between simulated and observed streamflow data was satisfactory considering the performance measures such as Nash–Sutcliffe efficiency (E), deviation runoff (Dv) and coefficient of determination (R2). The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to establish uncertainty bounds for the simulated flow using the Nash–Sutcliffe coefficient as a performance likelihood measure. Sensitivity analysis results indicate that runoff estimations are most sensitive to parameters related to climate conditions, soil and land use. These results state that even though climate conditions are generally most significant in water balance modeling, attention should also focus on land use characteristics as well. Specifically with respect to HSPF, the two most sensitive parameters, INFILT and LZSN, are both directly dependent on soil and land use characteristics.

Wasteload Allocation Using Wastewater Treatment and Flow Augmentation

A Wasteload allocation model, named Cost-Flow-Augmentation Model involving wastewater treatment and flow augmentation as a method of pollution abatement has been developed. The cost functions for wastewater treatment were developed as power functions of biochemical oxygen demand (BOD) removal using the regression module of the SPSS10 software. The cost function for flow augmentation was also developed using a regression between cost of dam/barrage and corresponding flow released from upstream reservoir for downstream water quality improvement. The response of wasteloads and flow augmentation on the water quality was quantified in terms of transfer coefficient calculated using the QUAL2E water quality simulation model. The performance of these models is demonstrated on the 22-km-long Delhi stretch of river Yamuna, India. Optimal solutions of the formulated models were obtained using the Web-based interactive non-differentiable interactive multiobjective bundle-based optimization system software. The optimal solutions obtained reveal that flow augmentation is not an economically feasible pollution abatement option for the Delhi stretch of river Yamuna.

Multi-criteria reservoir water management

The Plastiras dam was constructed in the late 1950s mainly for electric power production, but it has also partially covered irrigation needs and water supply of the plain of Thessaly. Later, the site has been designated as an environment conservation zone because of ecological and landscape values, while tourist activities have been developed around the reservoir. Irrigation of agricultural land, hydroelectric production, drinking water supply, tourism, ecosystem water quality and scenery conservation have evidently been conflicting targets for many years. Good management would require a multi-criteria decision making. Historical data show that the irregular water release has resulted in a great annual fluctuation of the reservoir water level. This situation could be improved by a rational management of abstractions. Apparently, higher release leads simultaneously to more power production and to irrigation of a larger agricultural land. Moreover, demands for electricity and for irrigation are partially competing to each other, due to different optimal time schedules of releases. On the other hand, higher water release leads to lower water level in the reservoir and, therefore, it decreases the beauty of the scenery and deteriorates the trophic state of the lake. Such degradation affects the tourist potential as well as the quality of drinking water supplied by the reservoir. A multi-criteria approach uses different scenarios for the minimum permissible water level of the reservoir, if a constant annual release is applied. The minimum level concept is a simple and functional tool, because it is understood by people, easily certified and incorporated into regulations. The quantity of water that would be yearly available is a function of the minimum level allowed. The water quality depends upon the trophic state of the lake, mainly the concentration of chlorophyll-a, which determines the state of eutrophication and is estimated by water quality simulation models, taking into account pollutant loads such as nitrogen and phosphorus. The value of the landscape is much depending on the water level of the lake, because for lower levels a dead-zone appears between the surface of the water and the surrounding vegetation. When this dead zone is large, it seems lifeless and the lake appears partially empty. Quantification of this visual effect is not easy, but it is possible to establish a correspondence between the aesthetic assessment of the scenery and the minimum allowed reservoir level. Using results from hydrological analysis, water quality models and landscape evaluation, it seems possible to construct a multi-criteria table with different criteria described against alternatives and with a plot of three relative indices against the minimum level allowed. However, decision making has to take into account the fact that comparison or merging of indices corresponding to different criteria analysis encompasses a degree of arbitrariness. More objective decisions would be possible if different benefits and costs were measured in a common unit. Moreover, management will be sensitive to different social pressures.

Cloud Simulation Platform for Water Environment Assessment

In this paper, a platform based on SaaS cloud computing framework is developed to provide professional online cloud simulation service for water quality assessment. The DLL, WINDOWS MFC programming methods are investigated and water pollution remote simulation can be performed supported by different water simulation softwares including MATLAB, MIKE and FLUENT. The C# language is used in our programming. The major steps of establishing a water quality simulation model using FLUENT and MATLAB are introduced.

Waste Load Allocation in Rivers using Fallback Bargaining

In this paper, bargaining process between different stakeholders involved in a waste load allocation problem is simulated using the Fallback Bargaining (FB) concept. The paper considers two main parties in a waste load allocation problem. On the one hand, there are wastewater dischargers intending to minimize their treatment costs and on the other hand, there is an environmental protection agency which monitors the river water quality at a checkpoint downstream of the location of dischargers. In this paper, different alternatives which are combinations of dischargers’ treatment scenarios are defined. A water quality simulation model is utilized to estimate the concentration of the water quality indicator along the river based on a selected alternative. If the concentration of water quality indicator in the selected checkpoint violates the water quality standards, a penalty function is used to calculate the amount of penalty assigned to dischargers. The allocated cost to each discharger is computed considering his treatment scenario as well as the penalty allocated to him. Two kinds of Fallback bargaining procedure termed as Unanimity Fallback Bargaining (UFB) and Fallback bargaining with Impasse (FBI), which both aim at minimizing the maximum dissatisfaction of bargainers in a negotiation problem, are utilized for finding a Compromise Set (CS) of alternatives. In this paper, the best alternative (alternatives) among CS members is (are) selected using a social choice theory namely Condorcet winner. The results of these two approaches are compared and the final alternative is selected which shows the initial Tradable Discharge Permits (TDPs) allocated to dischargers. Finally, in order to decrease the total allocated cost to dischargers, initial allocated TDPs are exchanged between them using the Extended Trading Ratio System (ETRS) developed by Mesbah et al. (Environ Model Software 24:238–246, 2009). The applicability and efficiency of the proposed methodology is investigated by applying it to a case study of the Zarjub River in the northern part of Iran.

Multi Objective Simulation-Optimization Approach in Pollution Spill Response Management Model in Reservoirs

In this study, a Pollution Spill Response Management Model (PSRMM) is developed to provide an emergency response on reservoir operation during accidental injection of hazardous material to reservoirs. PSRMM consist of spatial system analyzing (SSA) model, 2D hydrodynamic and water quality simulation model (CE-QUAL-W2), and multi-objective particle swarm optimization (MOPSO) algorithm. CE-QUAL-W2 model is applied for spatial and temporal analysis of water body in simulation routine of PSRMM. Also, in an advanced modeling framework, CE-QUAL-W2 is coupled with MOPSO algorithm to obtain desirable near optimal reservoir operation strategy and/or emergency planning in selective withdrawal framework. The simulation-optimization (SO) routine of PSRMM provides pareto optimum reservoir operation strategy in selective scheme to minimize reservoir cleanup time and to reduce the magnitude and frequency of water quality standard violations. The proposed tool is applied in Ilam reservoir in Iran, as a multipurpose hydraulic project providing water for drinking, irrigation, and flood control during an accident spill of conservative hazardous material. Different scenarios are defined and tested employing the proposed PSRMM for managing accidental spill of conservative pollutant into the reservoir.

Simulation-Based Inexact Two-Stage Chance-Constraint Quadratic Programming for Sustainable Water Quality Management under Dual Uncertainties

Water quality planning is complicated itself but further challenged by the existence of uncertainties and nonlinearities in terms of model formulation and solution. In this study, a simulation-based inexact two-stage chance-constraint quadratic programming (SITCQP) approach was developed for water quality management. The SITCQP model was a hybrid of two-stage stochastic programming (TSP), chance-constraint programming (CCP), inexact quadratic programming (IQP), and multisegment stream water quality simulation. A water quality simulation model was provided for reflecting the relationship between the pollution-control actions before wastewater dis- charge and the environmental responses after the discharge. Interval quadratic polynomials were employed to reflect the uncertainties and nonlinearities associated with the costs for wastewater treatment. Uncertainties derived from water quality standards were characterized as random variables with normal distributions. The proposed approach was applied to a hypothetical case in water quality management. The modeling solutions were presented as combinations of deterministic, interval and distributional information, which could facilitate predictions for different forms of uncertainties. The results were valuable for helping decision makers generate alternatives between wastewater treatment and stream water quality management. DOI: 10.1061/(ASCE)WR.1943-5452.0000328. © 2014 American Society of Civil Engineers. Author keywords: Inexact quadratic programming; Two-stage stochastic programming; Chance constraint programming; Water quality; Uncertainty; Biochemical oxygen demand.

Multi objective calibration of large scaled water quality model using a hybrid particle swarm optimization and neural network algorithm

Large scaled simulation models, especially the water quality simulation models, are so complicated that makes calibration processes huge tasks; in order to attain optimum solution, lots of parameters must be calibrated, simultaneously. Methods based on evolutionary algorithm developed new horizons in calibration procedure. Hybrid algorithms are of the newest. In hybrid algorithms, one of the modules is applied as a simulator and the other one takes role as an optimization module. In this article, overcoming these challenges, hybrid ANN-PSO algorithm is applied in calibration process of water quality model CE-QUAL-W2. Here, Particle Swarm Optimization (PSO) provides simulation (CE-QUAL-W2) model with sets of parameters to simulate model. Using these results, Neural Network (estimator) is trained. In the next step, simulator would be replaced with estimator and Artificial Neural Network (ANN) would estimate simulator’s behavior in a way less time. The first goal is to calibrate thermal parameter; going forward through this process needs water surface elevation parameter to be calibrated, too. As a result, the proposed model will become multi-objective one, applied in Karkheh reservoir in Iran during 6 month simulation period. The proposed approach overcomes the high computational efforts required if a conventional calibration search technique was used, while retaining the quality of the final calibration results. Estimator (ANN) embedded in optimization algorithm (PSO) in calibration process, undoubtedly, reduced run time while the answers have reliable quality.

Development of a Complex System Dynamic Eutrophication Model: Application to Karkheh Reservoir

Abstract The ever-increasing pollutant loads have significantly degraded the quality of receiving water bodies. High–resolution, spatially distributed water quality simulation models can be unsuitable at the planning/reconnaissance level of water resources decision making or for the rapid interactive analysis to support a participatory decision environment. This study presents a complex system dynamic (SD) eutrophication model in an object-oriented template to facilitate reservoir nutrient simulation process and assessment of alternative management strategies. The proposed model considers feedback and interactive loops between phytoplanktons, herbivorous zooplanktons, carnivorous zooplanktons, particulate organic matter, dissolved organic matter, ammonium, nitrate, phosphorus, total suspended solids, and dissolved oxygen in the simulation process. The reservoir SD eutrophication model is structured and expressed as a series of 30 differential equations for 10 state variables and three segmented layers. Th...

A Bayesian approach for calculating variable total maximum daily loads and uncertainty assessment

To account for both variability and uncertainty in nonpoint source pollution, one dimensional water quality model was integrated with Bayesian statistics and load duration curve methods to develop a variable total maximum daily load (TMDL) for total nitrogen (TN). Bayesian statistics was adopted to inversely calibrate the unknown parameters in the model, i.e., area-specific export rate (E) and in-stream loss rate coefficient (K) for TN, from the stream monitoring data. Prior distributions for E and K based on published measurements were developed to support Bayesian parameter calibration. Then the resulting E and K values were used in water quality model for simulation of catchment TN export load, TMDL and required load reduction along with their uncertainties in the ChangLe River agricultural watershed in eastern China. Results indicated that the export load, TMDL and required load reduction for TN synchronously increased with increasing stream water discharge. The uncertainties associated with these estimates also presented temporal variability with higher uncertainties for the high flow regime and lower uncertainties for the low flow regime. To assure 90% compliance with the targeted in-stream TN concentration of 2.0mgL−1, the required load reduction was determined to be 1.7×103, 4.6×103, and 14.6×103kgTNd−1 for low, median and high flow regimes, respectively. The integrated modeling approach developed in this study allows decision makers to determine the required load reduction for different TN compliance levels while incorporating both flow-dependent variability and uncertainty assessment to support practical adaptive implementation of TMDL programs.

General Multiobjective Model and Simulated Annealing Algorithm for Waste-Load Allocation

Waste-load allocation (WLA) is a difficult problem with multiobjective features, which requires models that consider a full range of competing goals to identify good and practical solutions. In this study, a general and multiobjective optimization model was proposed. This model integrates different decision variables related to multiple waste-removal efficiencies and outflow discharges into water bodies. This model also considered an equity measure and limits for the concentration of dissolved oxygen and biochemical oxygen demand. A simulated annealing (SA) algorithm and the enhanced stream water quality simulation model (QUAL2E) were used to solve the WLA problem. This approach was applied to the Santa Maria da Vitória River watershed in the state of Espírito Santo, Brazil. Computational results have demonstrated that the proposed optimization model with the SA metaheuristic and QUAL2E could effectively incorporate the expectations and conflicting objectives, providing various good solutions to support decision makers.

Climate change induced risk in water quality control problems

Summary A modeling framework is presented in this paper, integrating hydrologic scenarios projected from a General Circulation Model (GCM) with a water quality simulation model to quantify the future expected risk. Statistical downscaling with a Canonical Correlation Analysis (CCA) is carried out to develop the future scenarios of hydro-climate variables starting with simulations provided by a GCM. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold quality level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) presented in an earlier study is then used to develop adaptive policies to address the projected water quality risks. Application of the proposed methodology is demonstrated with the case study of Tunga–Bhadra river in India. The results showed that the projected changes in the hydro-climate variables tend to diminish DO levels, thus increasing the future risk levels of LWQ.

Optimization Model for BMP Placement in a Reservoir Watershed

The purpose of this study was to develop an optimization model for the optimal placement of structural best management practices (BMPs) at the watershed scale for a reservoir. In this research, the authors considered three types of structural BMPs (i.e., a wet detention pond, a grass swale, and an infiltration trench). The complete model consists of three interacting submodels: a watershed water quality simulation model [the Hydrologic Simulation Program-FORTRAN (HSPF) model], a reservoir water quality model (the CE-QUAL-W2 model); and an optimization model that uses a well-developed genetic algorithm (GA) software. This research used the Feitsui Reservoir and its watershed in northern Taiwan as the area of application. For the optimization model, the objective function was to minimize the total annual cost of BMPs, within the constraints of achieving minimum water quality standards for the concentrations of phosphorus, ammonium, nitrate-nitrite and for the total suspended solids in the reservoir. The study first simulated the discharge and pollutant loads for the watershed in 2002–2003 by using HSPF. The reservoir model CE-QUAL-W2 was then used to simulate variations in pollutant concentrations in the reservoir on the basis of the input discharge and pollutant loads from HSPF. The optimization model was finally used to search for the near-optimal selection and placement of BMPs in the watershed. The results obtained from the completed model effectively demonstrate its viability in improving water quality in the reservoir by adopting optimal BMPs placement strategies.

Sustainable environmental flow management based on lake quality protection: The case of Baiyangdian Lake, China

Abstract Water shortages and quality deterioration are serious dilemmas for lake managers as both problems sharply decrease the ecosystem services and functions provided by a lake. In this paper, using WASP as a tool, we have established and calibrated a water quality simulation model for Baiyangdian Lake and then analyzed water quality variations in scenarios with different pollution load reductions and different environmental flow releases into the lake and discovered the sustainable schemes for environmental flow management. From the results, in the scenarios of 30%, 50%, and 90% pollutant load reduction, the sustainable environmental flows are 3.52, 3.13, and 2.12 m3/s, and the corresponding lake levels could attain 7.45 m, 7.35 m, and 7.05 m from the initial 6.0 m. The obtained results indicated that water release could improve the lake volume and quality simultaneously.

SIFNP: Simulation-Based Interval-Fuzzy Nonlinear Programming for Seasonal Planning of Stream Water Quality Management

A simulation-based interval-fuzzy nonlinear programming (SIFNP) approach was developed for seasonal planning of stream water quality management. The techniques of inexact modeling, nonlinear programming, and interval-fuzzy optimization were incorporated within a general framework. Based on a multi-segment stream water quality simulation model, dynamic waste assimilative capacity of a river system within a multi-season context was considered in the optimization process. The method could not only address complexities of various system uncertainties but also tackle nonlinear environmental–economic interrelationships in water quality management problems. In addition, interval-fuzzy numbers were introduced to reflect the dual uncertainties, i.e., imprecision associated with fixing the lower and upper bounds of membership functions. The proposed method was applied to a case of water quality management in the Guoyang section of the Guo River in China. Interval solutions reflecting the inherent uncertainties were generated, and a spectrum of cost-effective schemes for seasonal water quality management could thus be obtained by adjusting different combinations of the decision variables within their solution intervals. The results indicated that SIFNP could effectively communicate dual uncertainties into the optimization process and help decision makers to identify desired options under various complexities of system components.