RESUMEN
Previous research has confirmed that the sediments at the bed of combined sewer systems are the main source of particulate and organic pollution during rain events contributing to combined sewer overflows. However, existing urban stormwater models utilize inappropriate sediment transport formulas initially developed from alluvial hydrodynamics. Recently, a model has been formulated and profoundly assessed based on laboratory experiments to simulate the erosion of sediments in sewer pipes taking into account the increase in strength with depth in the weak layer of deposits. In order to objectively evaluate this model, this paper presents a Bayesian analysis of the model using field data collected in sewer pipes in Paris under known hydraulic conditions. The test has been performed using a MCMC sampling method for calibration and uncertainty assessment. Results demonstrate the capacity of the model to reproduce erosion as a direct response to the increase in bed shear stress. This is due to the model description of the erosional strength in the deposits and to the shape of the measured bed shear stress. However, large uncertainties in some of the model parameters suggest that the model could be over-parameterised and necessitates a large amount of informative data for its calibration.
Asunto(s)
Sedimentos Geológicos , Modelos Teóricos , Aguas del Alcantarillado , Teorema de Bayes , Conservación de los Recursos Naturales , Recolección de Datos , Mecánica , Lluvia , Suelo , Movimientos del Agua , Contaminantes del Agua/análisisRESUMEN
Estimating the level of uncertainty in urban stormwater quality models is vital for their utilization. This paper presents the results of application of a Monte Carlo Markov Chain method based on the Bayesian theory for the calibration and uncertainty analysis of a storm water quality model commonly used in available software. The tested model uses a hydrologic/hydrodynamic scheme to estimate the accumulation, the erosion and the transport of pollutants on surfaces and in sewers. It was calibrated for four different initial conditions of in-sewer deposits. Calibration results showed large variability in the model's responses in function of the initial conditions. They demonstrated that the model's predictive capacity is very low.
Asunto(s)
Lluvia/química , Aguas del Alcantarillado/química , Incertidumbre , Abastecimiento de Agua/normas , Teorema de Bayes , Calibración , Ciudades , Francia , Cadenas de Markov , Modelos QuímicosRESUMEN
In this paper we present a benchmarking methodology, which aims at comparing urban runoff quality models, based on the Bayesian theory. After choosing the different configurations of models to be tested, this methodology uses the Metropolis algorithm, a general MCMC sampling method, to estimate the posterior distributions of the models' parameters. The analysis of these posterior distributions allows a quantitative assessment of the parameters' uncertainties and their interaction structure, and provides information about the sensitivity of the probability distribution of the model output to parameters. The effectiveness and efficiency of this methodology are illustrated in the context of 4 configurations of pollutants' accumulation/erosion models, tested on 4 street subcatchments. Calibration results demonstrate that the Metropolis algorithm produces reliable inferences of parameters thus, helping on the improvement of the mathematical concept of model equations.
Asunto(s)
Modelos Teóricos , Incertidumbre , Movimientos del Agua , Algoritmos , Teorema de Bayes , Calibración , Ciudades , Simulación por Computador , Lluvia , Reproducibilidad de los ResultadosRESUMEN
In environmental modelling, estimating the confidence level in conceptual model parameters is necessary but difficult. Having a realistic estimation of the uncertainties related to the parameters is necessary i) to assess the possible origin of the calibration difficulties (correlation between model parameters for instance), and ii) to evaluate the prediction confidence limits of the calibrated model. In this paper, an application of the Metropolis algorithm, a general Monte Carlo Markov chain sampling method, for the calibration of a four-parameter lumped urban stormwater quality model is presented. Unlike traditional optimisation approaches, the Metropolis algorithm identifies not only a "best parameter set", but a probability distribution of parameters according to measured data. The studied model includes classical formulations for the pollutant accumulation during dry weather period and their washoff during a rainfall event. Results indicate mathematical shortcomings in the pollutant accumulation formulation used.