RESUMEN
Reservoir hydrodynamic and water quality modeling, in conjunction with the monitoring programs, is one of the essential tools for controlling the pollution of these types of water bodies. The complexity of the model, data scarcity, and the variable nature of natural phenomena lead to uncertainty in models, which should be considered in the calibration process of these models. Uncertainty-based automatic calibration is one of the methods that can be effective in achieving a high-reliability model. In this paper, the Sequential Uncertainty Fitting (SUFI-2) algorithm was used for the automatic calibration of the two-dimensional hydrodynamic and water quality model (CE-QUAL-W2) for the reservoir under parameter uncertainty conditions. To this end, the CE-QUAL-W2 model was developed to simulate the temperature and water surface elevation of the Karkheh Dam reservoir (western Iran). The parameters affecting temperature were regarded as uncertain parameters in the calibration process, including the coefficients of longitudinal eddy viscosity, longitudinal eddy diffusivity, Chezy coefficient or Manning, wind sheltering, solar radiation absorbed in the surface layer, extinction coefficient for pure water, and the experimental coefficients of wind speed function. The developed method demonstrated a high potential for matching the simulated temperature and water surface elevation for the reservoir with the measured data. Averagely, 69% of the simulated temperature and 90% of the simulated water surface elevation were located within the 95% confidence interval. The SUFI-2 algorithm also showed better performance in terms of the convergence rate compared with the particle swarm optimization (PSO) algorithm, which indicated a lower number of calls (80 calls compared to 2000 calls) and could reduce the total root-mean-square error by 9.6%.
Asunto(s)
Hidrodinámica , Calidad del Agua , Calibración , Monitoreo del Ambiente , Irán , Modelos Teóricos , Reproducibilidad de los Resultados , IncertidumbreRESUMEN
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.
Asunto(s)
Monitoreo del Ambiente , Calidad del Agua , Irán , Modelos Teóricos , IncertidumbreRESUMEN
This paper introduces a Semivariance-Transinformation (S-T) based method for designing an optimum bay water nutrients monitoring network in San Francisco bay (S.F. bay), USA. Phosphorus and nitrogen are the most important nutrients that lead to eutrophic condition. The monthly phosphate and nitrate+nitrite data recorded during September 2006 to August 2015 was obtained over 14 active stations located at S.F. bay and was used in the research. Semivariance and discrete transinformation entropy have been applied to calculate the optimum range of the monitoring distance. The study indicated the ranges of 28 to 82 and 37 to 50km for the phosphate and nitrate+nitrite respectively. Useful information can be obtained from the monitoring network, if the monitoring distance is included in the mentioned intervals. The findings of the research introduce a new approach in the field of water quality monitoring networks design.
Asunto(s)
Bahías/química , Monitoreo del Ambiente/métodos , Nitrógeno/análisis , Fósforo/análisis , Proyectos de Investigación , Contaminantes Químicos del Agua/análisis , Análisis de Varianza , Entropía , San Francisco , Calidad del AguaRESUMEN
In this paper, a new systematic approach is designed to maximize the demand coverage and receiving waste load by river-reservoir systems while enhancing water quality criteria. The approach intends to control the reservoir eutrophication while developing a trade-off between the maximum receiving load and shortage on demand coverage. To simulate the system, a hybrid process-based and data-driven model is tailored. Initially, the two-dimensional hydrodynamics and water quality simulation model (CE-QUAL-W2) is linked with an effective single and/or multiple optimization algorithms (PSO) to evaluate the proposed scenarios. To increase the computational efficiencies, the simulation model is substituted with a surrogate model (ANN) in an adaptive-dynamically refined routine. The proposed method is illustrated by a case study in Iran, namely, Karkheh River Reservoir, for 180-monthly periods. The results showed the applicability of the methodology especially to solve high-dimensional multi-period complex water resource optimization problems. Also, the results demonstrated that eutrophication could be reduced under the optimal inflow phosphate control and reservoir operation, regulating the total phosphorous concentration in the reservoir.
Asunto(s)
Monitoreo del Ambiente , Eliminación de Residuos Líquidos/métodos , Calidad del Agua/normas , Algoritmos , Eutrofización , Hidrodinámica , Irán , Modelos Teóricos , Fósforo/análisis , Ríos/química , Eliminación de Residuos Líquidos/normas , Eliminación de Residuos Líquidos/estadística & datos numéricos , Recursos Hídricos , Abastecimiento de AguaRESUMEN
Assessment and redesign of water quality monitoring networks is an important task in water quality management. This paper presents a new methodology for optimal redesign of groundwater quality monitoring networks. The measure of transinformation in discrete entropy theory and the transinformation-distance (T-D) curves are used to quantify the efficiency of sampling locations and sampling frequencies in a monitoring network. The existing uncertainties in the T-D curves are taken in to account using the fuzzy set theory. The C-means clustering method is also used to classify the study area to some homogenous zones. The fuzzy T-D curve of the zones is then used in a multi-objective hybrid genetic algorithm-based optimization model. The proposed methodology is utilized for optimal redesign of monitoring network of the Tehran aquifer in the Tehran metropolitan area, Iran.