Complex Water Quality Model Research Articles

Modelos zero-dimensionais como ferramenta para planejamento e gestão de qualidade da água em reservatórios

ABSTRACT Water quality models are often applied to support the definitions for water resources planning and management. This process requires appropriate data to calibrate and make the required verification process for validation analysis. However, in some case studies, there is not enough data to run a complex water quality model whatever is the water system, being river or reservoir. In the context of scarce data, the search for a simple water quality model is an alternative to comprehend the water quality behavior of the water system. In this research, an unsteady zero-dimensional (0D) water quality model was developed and applied using synthetic series of phosphorus concentrations for a Brazilian reservoir. The results of the zero-dimensional models developed in this research were compared to the results of a three-dimensional water quality model, in order to verify if this methodology can be a simpler alternative to a complex three-dimensional model. The compatible results of the zero-dimensional (0D) and three-dimensional (3D) models show the simplified numerical schemes are important potential tools to be considered for initial analysis of water quality dynamics in reservoirs and for management purposes.

Pi-VAT: A web-based visualization tool for decision support using spatially complex water quality model outputs

Effective watershed management and protection of water resources from non-point source pollution require identification, prioritization, and targeting of pollutant source areas. Process-based hydrology and water quality models are powerful heuristic tools for land and water resources managers. However, because of their complexity, such models are often under-utilized as management prioritization and planning tools. In this paper, we present a prioritization, interactive visualization, and analysis tool (Pi-VAT) that is programmed to synthesize multi-scenario, multi-watershed outputs from process-based geospatial models. We demonstrate the utility of Pi-VAT to examine simulated hydrologic, sediment, and water quality response at the hillslope/hydrologic response unit (HRU) scale. We apply Pi-VAT to output from multiple watersheds and for multiple management scenarios and treatments from two geospatial models for watershed management: Water Erosion Prediction Project (WEPP) and Soil & Water Assessment Tool (SWAT). Pi-VAT was developed using the Shiny web application framework for the R programming language. In a matter of minutes, Pi-VAT can synthesize overwhelming amounts of output from process-based models into information useful for land and water resources managers. We illustrate the use of Pi-VAT to interactively identify, quantify, and visualize areas that are most susceptible to disturbance under different scenarios and provide a synthesis approach based on land use, soil type, and slope steepness. This approach guides land and water resources managers in prioritizing the areas of the watershed that provide the maximum reduction in pollutant loads while treating the least amount of area. Pi-VAT provides a flexible reactive platform for the development of decision support tools based on process-based models intended for watershed management and research applications.

Modelling phosphorus inflow to the Mozhayskoe reservoir with the HYPE hydrological model

Biogenic flow is the determining factor of ecological well-being of water bodies. It depends on a number of factors, such as weather conditions, soil and vegetation cover, agricultural use of the catchment area. Its simulation is possible based on a complex water quality model with parameters distribution. In this paper, we show that the model calculates the water flow with satisfactory accuracy and gives reliable values of phosphorus flow in the investigated river outlet. The influence of dryness of the year on the phosphorus flow is important and reduces dissolved phosphorus flow several times. The results of experiments with the model show a decrease of dissolved phosphorus flow subsequent to cease of fertilizing in range from 5 to 11%. The values of the surface and groundwater genetic components of phosphorus flow are comparable, while soil component amounts 65% of local phosphorus flow.

Bayesian framework of parameter sensitivity, uncertainty, and identifiability analysis in complex water quality models

An efficient Bayesian analytical framework was developed to address the challenges of uncertainty analysis and assess the parameter identification problems of complex water quality models with high-dimensional parameter space. The inclusion of a multi-chain Markov Chain Monte Carlo method and comprehensive global sensitive analysis (GSA) guarantees the results to be robust. A high-frequency synthetic data case study was conducted in the EFDC water quality module including 54 parameters. The comprehensive GSA identified 39 completely or partially sensitive parameters for reducing dimensionality, among which only nine were identifiable without significant bias. The fundamental causes of the parameter identification problem could be traced to the cognitive limitations of the real water quality assessment process instead of data scarcity. The framework is powerful for exploring these limitations, generating reminders for model users to use Bayesian estimates in future forecasts, and providing directions for model developers to perfect a model in future work.

Global Sensitivity Analysis of a Water Quality Model in the Three Gorges Reservoir

Global sensitivity analysis is the key to establishing advanced and complex water quality models and measurements of ecological parameters. In this paper, the Sobol’s sensitivity analysis method was applied to a quantitative analysis of the important factors governing a water quality model, which has been developed to simulate algal dynamics in Caotang Bay, one of the tributary bays in the Three Gorges Reservoir, China. The analysis focused on the response of chlorophyll-a and dissolved oxygen to 11 parameters. The results show that chlorophyll-a is influenced mainly by the maximum phytoplankton growth rate, the lower optimum temperature for algal growth, the phosphate half-saturation constant, and the phytoplankton linear mortality rate; while dissolved oxygen is influenced mainly by the maximum phytoplankton growth rate, the lower optimum temperature for algal growth, the phytoplankton basal respiration rate, and the detritus remineralization rate. These parameter sensitivities change with time and have a marked seasonal pattern. The parameter sensitivity differences between a shallow lake or reservoir and a deep reservoir suggest that mechanisms of cycling in nutrients and dissolved oxygen are different.

Parameter Estimation of Water Quality Models Using an Improved Multi-Objective Particle Swarm Optimization

Water quality models are of great importance for developing policies to control water pollution, with the model parameters playing a decisive role in the simulation results. It is necessary to introduce estimation through multi-objective parameters, which is often affected by noise in the data, into water quality models. This paper presents a multi-objective particle swarm optimization algorithm, which is based on the Mahalanobis distance operation, mechanism of cardinality preference and advection-diffusion operator. The Mahalanobis distance operation can effectively reduce the influence of noise in the data on model calibration. The mechanism of cardinality preference and the use of the advection-diffusion operator can prevent non-dominated solutions from falling into the local optimum. Four cases were used to test the proposed approach. The first two cases with true Pareto fronts show that this approach can accurately estimate the true Pareto front with a good distribution, even in the presence of noise. Furthermore, the application of the approach was tested by the O’Connor model and Crops of Engineers Integrated Compartment Water Quality Model. We show that our approach can produce satisfactory results for the multi-objective calibration of complex water quality models. In general, the proposed approach can provide accurate and efficient parameter estimation in water quality models.

Multiobjective Calibration of Reservoir Water Quality Modeling Using Multiobjective Particle Swarm Optimization (MOPSO)

Water resource management encounters large variety of multi objective problems that require powerful optimization tools in order to fully characterize the existing tradeoffs between various objectives that can be minimizing difference between forecasted physical, chemical, and biological behaviors of model and measured data. Calibration of complex water quality models for river and reservoir systems may include conflicting objectives addressed by various combinations of interacting calibration parameters. Calibration of the two dimensional CE-QUAL-W2 water quality and hydrodynamic model is an excellent example where the model must be calibrated for both hydrodynamic and water quality behavior. The aim of the present study is to show how multiobjective particle swarm optimization (MOPSO) can be implemented for automatic calibration of water quality and hydrodynamic parameters of a 2-dimensional, hydrodynamic, and water quality models (CEQUAL-W2) to predict physical, chemical, and biological behaviors of a water body, and then focus on a relevant case study. So MOPSO is utilized to generate Pareto optimal solutions for two conflicting calibration objectives. A combined measure of thermal and reservoir water level is considered as the first calibration objective. The second objective is formulated to forecast the best physical, chemical, and biological behavior of the model. Realizing the strong interactions between water quality and hydrodynamic issues of water bodies and their dependencies on the same set of calibration parameters, the proposed multiobjective approach may provide a wide version of all possible calibration solutions for better decision making to select best solution from pareto front.

Research Platform for Monitoring, Control and Security of Critical Infrastructure Systems

Sustainable operation of Critical Infrastructure Systems (CISs) is of a major concern to modern societies. Monitoring, control and security of such systems plays a key role in guaranteeing continuous, reliable and above all secure access to the resources provided by these systems. Development of adequate software and hardware structures, as well as algorithms to perform such functions cannot be done apart from the operational conditions of the plant. On the other hand the dependencies between the resources provided by the CIS and the societies, prevents from experiments. Also this approach could be found costly and hard to justify in the world driven by hard economics. This, once again calls for the simulation tools to be exploited. Due to the vast complexity of these systems including spatial distribution over a wide area, a control engineering based multiagent framework is often utilised to cope with the issues of monitoring, control and security of CIS. Since there is no off the shelve solution for the development of such systems a novel, interesting, approach is proposed within this work to cope with the CIS simulation environment. The environment is based on the JADE and Matlab. A simple, but computational demanding example is provided, presenting the abilities and performance of the Research Platform. The simulation example is presented in the context of the new complex water quality model.

Uncertainty estimation of a complex water quality model: The influence of Box–Cox transformation on Bayesian approaches and comparison with a non-Bayesian method

In urban drainage modelling, uncertainty analysis is of undoubted necessity. However, uncertainty analysis in urban water-quality modelling is still in its infancy and only few studies have been carried out. Therefore, several methodological aspects still need to be experienced and clarified especially regarding water quality modelling. The use of the Bayesian approach for uncertainty analysis has been stimulated by its rigorous theoretical framework and by the possibility of evaluating the impact of new knowledge on the modelling predictions. Nevertheless, the Bayesian approach relies on some restrictive hypotheses that are not present in less formal methods like the Generalised Likelihood Uncertainty Estimation (GLUE). One crucial point in the application of Bayesian method is the formulation of a likelihood function that is conditioned by the hypotheses made regarding model residuals. Statistical transformations, such as the use of Box–Cox equation, are generally used to ensure the homoscedasticity of residuals. However, this practice may affect the reliability of the analysis leading to a wrong uncertainty estimation. The present paper aims to explore the influence of the Box–Cox equation for environmental water quality models. To this end, five cases were considered one of which was the “real” residuals distributions (i.e. drawn from available data). The analysis was applied to the Nocella experimental catchment (Italy) which is an agricultural and semi-urbanised basin where two sewer systems, two wastewater treatment plants and a river reach were monitored during both dry and wet weather periods. The results show that the uncertainty estimation is greatly affected by residual transformation and a wrong assumption may also affect the evaluation of model uncertainty. The use of less formal methods always provide an overestimation of modelling uncertainty with respect to Bayesian method but such effect is reduced if a wrong assumption is made regarding the residuals distribution. If residuals are not normally distributed, the uncertainty is over-estimated if Box–Cox transformation is not applied or non-calibrated parameter is used.

Uncertainty in water quality modelling: The applicability of Variance Decomposition Approach

Quantification of uncertainty is of paramount interest in integrated urban drainage water quality modelling. Indeed, the assessment of the reliability of the results of complex water quality models is crucial in understanding their significance. However, the state of knowledge regarding uncertainties in urban drainage models is poor. In the case of integrated urban drainage water quality models, due to the fact that integrated approaches are basically a cascade of sub-models (simulating the sewer system, wastewater treatment plant and receiving water body), uncertainty produced in one sub-model propagates to the following ones in a manner dependent on the model structure, the estimation of parameters and the availability and uncertainty of measurements in the different parts of the system. Uncertainty basically propagates throughout a chain of models in which the simulation output from upstream models is transferred to the downstream ones as input. The Variance Decomposition Approach tracks uncertainty propagation commonly assuming that the correlation among error sources is negligible. In complex environmental models, the overall uncertainty can differ significantly from the simple sum of uncertainties generated in each sub-model, showing the well-known uncertainty accumulation problems due to non-linearity in the model and correlation among the sources of uncertainty. This work discusses the importance of such issues in the application of a complex integrated urban drainage model with the aim of evaluating the applicability of Variance Decomposition Approach. The integrated model and the methodology for the uncertainty decomposition were then applied to a complex integrated catchment: the Nocella basin (Italy). The results showed that the Variance Decomposition Approach can be a powerful tool for uncertainty analysis, but a possible correlation among uncertainty sources should be considered because it can greatly affect the analysis.

Linear Superposition in Total Maximum Daily Loads Applications: The Steady‐State Response Matrix Revisited1

Abstract: The steady‐state response matrix has historically proved a valuable tool in computing the water quality response to loadings and in providing insight into the relative impact of individual loading sources. The insight obtained may be is particularly useful in modern applications of increasingly complex water quality models to problems involving multiple point and nonpoint sources, such as in the assessment of total maximum daily loads (TMDLs). Where appropriate and the underlying equations linear, the steady‐state response matrix can be used to synthesize the results of more complicated models and present them in a way easily understood by policy makers. A straightforward method is presented for generating the response matrix using complex models, and example applications discussed. Example applications include a simple demonstration; incorporation of the method into the Mississippi Department of Environmental Quality’s STREAM model used in TMDL development; a TMDL modeling study of the Grand Calumet River and Indiana Harbor Canal, Indiana, using CE‐QUAL‐ICM; and a TMDL modeling study of the Big Sunflower River, Mississippi, using the Water Analysis Simulation Program model.

A sensitivity study for the Amstel River Basin water quality model

A sensitivity analysis with the FAST method (Fourier Amplitude Sensitivity Test) was carried out with an application of the Sobek model code to the Amstel River Basin. The sensitivity analysis was used to study the contribution of six model parameters to the number of days the chloride concentration exceeds the water quality standards and the summer averaged total-phosphorus concentration at one location in the river network. The aim of this study was to test whether a sensitivity analysis (SA) with the FAST method is feasible for complex water quality model applications, such as networks of rivers and channels and eutrophication models. Even if a lot of a priori knowledge about the water system is needed for the selection of relevant input parameters and representative output parameters and locations, the use of SA methodologies appears to be a very useful tool in benchmarking river basin models.

Uncertainty and risk in water quality modelling and management

The case is presented for increasing attention to the evaluation of uncertainty in water quality modelling practice, and for this evaluation to be extended to risk management applications. A framework for risk-based modelling of water quality is outlined and presented as a potentially valuable component of a broader risk assessment methodology. Technical considerations for the successful implementation of the modelling framework are discussed. The primary arguments presented are as follows. (1) For a large number of practical applications, deterministic use of complex water quality models is not supported by the available data and/or human resources, and is not warranted by the limited information contained in the results. Modelling tools should be flexible enough to be employed at levels of complexities which suit the modelling task, data and available resources. (2) Monte Carlo simulation has largely untapped potential for the evaluation of model performance, estimation of model uncertainty and identification of factors (including pollution sources, environmental influences and ill-defined objectives) contributing to the risk of failing water quality objectives. (3) For practical application of Monte Carlo methods, attention needs to be given to numerical efficiency, and for successful communication of results, effective interfaces are required. A risk-based modelling tool developed by the authors is introduced.