Abstract
Multiphysics urban flood models are commonly used for urban
infrastructure development planning and evaluating risk due to climate
change and sea level rise. However, 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, often
without observable data. 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 in combination with machine learning, data and computer
science, epidemiology, socioeconomics, and transportation and electrical
engineering to minimize 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). Through a sensitivity study, 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 parameters using open databases. We employ
the multilevel Monte Carlo (MLMC) 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 results show that the uncertainty in the flood predictions (as
described by the coefficient of variation of the flood water depth) 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. Our results 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.