State Water Resources Research Institute Program
Project ID: 2009DC100B
Title: Modeling Model Uncertainty for Storm Water Quantity and Quality Analysis
Project Type: Research
Start Date: 3/01/2009
End Date: 2/28/2010
Congressional District: District of Columbia
Focus Categories: Models, Methods, Wastewater
Keywords: Modeling, SWMM, Uncertainty, Wastewater
Principal Investigators: Bejleri, Valbona; Deksissa, Tolessa
Federal Funds: $ 14,685
Non-Federal Matching Funds: $ 478,73
Abstract: The motivation for this research comes from the demand for reliable estimates, which are directly related to the management decisions for reducing combined sewer overflows (CSO) or improving surface water quality in the District of Columbia. In environmental or water resource management, mathematical models become more attractive tools than monitoring due to their prediction capacity and cost effectiveness. Model output is an estimate of the real measurement, and therefore its reliability depends partly on the relevance of model parameters and data gathered. The purpose of this research is to develop statistical tools that will help to accurately measure the uncertainty in the input, output and model parameters, toward development of a model and statistical methods that will adjust for both model and parameter uncertainties. The general nature of the results of this research will allow the methodology developed to apply in any mathematical model used in environmental or water resource management decision making. This project will help to understand better the behavior of an environmental model and adjust for uncertainty. Implementation of our results/findings may contribute to a substantial decrease of the uncertainty of the parameter estimates and model outputs. It will assist in management decisions and in formulating policies of the District of Columbia.
From the educational point of view, this project will attract students toward the road of research by engaging students in solving real life problems. It will build new bridges of cooperation across different disciplines. Students will be trained to use model simulation software and perform data analysis with SAS. The results of this project will be disseminated through the water quality conferences, or statistical meetings.
Progress/Completion Report, 2009, PDF