Parameter sensitivity and uncertainty analysis in simplified conceptual urban drainage models

Cintia Brum Siqueira Dotto
2017
Stormwater models are powerful tools to aid the planning, design and performance of different stormwater management strategies. Although these models provide a great platform for decision making, they all have an intrinsic level of uncertainty. Little is understood about the sources and magnitude of this uncertainty, which could be due to the errors in measured data (input and calibration data) and/or due to the model itself. To better understand these sources and their impacts on the model
more » ... ts on the model predictions, robust model calibration and sensitivity analysis should be performed. The methodologies used for such an exercise should not only be able to provide an assessment of the uncertainties in the model's parameter values and an evaluation of the confidence level of the model's predictions, but also be able to identify and propagate the different sources of uncertainties. The main aim of this research project is to assess uncertainties in conceptual urban stormwater flow and pollution generation models, with different levels of complexity, by evaluating the impact of different sources of uncertainties on the model predictions and parameter sensitivity. The research focuses on three main steps: (i) identifying suitable global sensitivity analysis method(s) to perform parameter calibration, model sensitivity and uncertainty analysis in stormwater models; (ii) exploring parameter calibration, model sensitivity and the resulting predictive uncertainties in models with different level of complexities; and, (iii) investigating the impact of measured input and calibration data uncertainty on the performance, sensitivity and predictive uncertainty of stormwater models. Four methods were applied for calibration, sensitivity and uncertainty analysis of a simple stormwater (quantity and quality) model: one is a formal Bayesian approach, and three are methods based on Monte Carlo simulations coupled with different sampling and acceptance criteria. While the application of the four methods generated similar posterior parameter dist [...]
doi:10.4225/03/58b500f23fba5 fatcat:k6ppzj7fgbgtrm6ehwq2racm74