Abstract
Multiphysics urban flood models are commonly used for urban infrastructure development planning and evaluating risk due to climate change and sea level rise. These integrated flood models rely on several parameters that are hard to measure directly and the resulting uncertainty in model prediction needs to be quantified. As a part of the Urban Flooding Open Knowledge Network (UFOKN) project, in this study we quantify parametric uncertainty in urban flood models. UFOKN incorporates flood model predictions and other areas of study and aims to reduce economic and human losses from future urban flooding in the United States. As a case study, we choose the Interconnected Channel and Pond Routing (ICPR) numerical model to simulate flooding in the city of Minneapolis in response to the design storms, e.g., 100-year rainfall.
We reduce the number of uncertain model parameters to the Manning’s roughness coefficient and vertical hydraulic conductivity of soil, and construct the distributions of these input parameters using open databases. Our results show that the uncertainty in the flood predictions is distributed highly non-uniformly in the urban area with the coefficient of variation exceeding 0.5 limited to a relatively few computational elements in the ICPR model. Moreover, the uncertainty due to Manning’s roughness coefficient and vertical hydraulic conductivity result different distribution of localized uncertainty but similar distribution of mean flood depth. We also employ the multilevel Monte Carlo method that combines a small number of high-resolution ICPR simulations with a larger number of low-resolution simulations to reduce the computational cost of computing the key statistics of the quantities of interest describing the urban flooding. Our findings demonstrate that urban flood models such as ICPR can provide reliable flood predictions and can be used for a targeted data acquisition to further reduce the parametric uncertainty.
Bio
Amir Kohanpur is a Postdoctoral Research Associate in the Department Civil and Environmental Engineering at the University of Illinois Urbana-Champaign. He received his B.Sc. in Mechanical Engineering from Shiraz University (2011), M.Sc. in Mechanical Engineering from Sharif University of Technology (2013), and Ph.D. in Civil Engineering from the University of Illinois Urbana-Champaign (2020). His doctoral work is related to pore-scale modeling of flow in porous media. Currently, he is working on uncertainty quantification and data-driven modeling with application to urban flooding.
