NSF Award
2210840
This Critical Aspects of Sustainability - Climate (CAS-Climate) project develops an accurate and fast statistical representation (also known as emulation) of large-scale flooding events as an alternate to running complex physical models in every instance. This is achieved by training the statistical formulation on representative flood simulations but adding flexibility to infer results for situations that are not in the training set. Large floods are expected to occur more frequently due to climate change and so motivate increased attention for robust, efficient, and real-time flood modeling. For most river engineering problems, including flood mitigation, there is a need for quantification of water depth and surface elevation under different scenarios of rainfall and storm surges. While many hydraulic models (e.g., the TELEMAC model) can represent these features, large-scale hydraulic simulations of flooding using high-resolution topographic and infrastructure data, are computationally expensive to complete. Thus, despite these modeling resources, simulating possible flood scenarios routinely or in a rapid manner is limited. A novel aspect of this work is to focus on statistical distributions for extreme events, which are appropriate for flooding, rather than average quantities that are less useful for assessing risks and hazards. Also, the statistical basis of this approach lends itself to measures of uncertainty in the predictions and so support decision making and other practical uses.<br/><br/>There is little existing methodology to emulate computer models built on spatial extreme distributions to represent flooding in a complex domain and an important feature of this work is matching the flooding response with the appropriate statistical processes. This is in contrast with a machine learning, purely data-driven approach where extreme events may not be as well represented. The modeling framework is transformative including: a Markov random field formulation using generalized extreme value distributions for site-specific conditional distributions, selecting neighborhood size using a penalized likelihood method for spatial extremes, a theoretical investigation into the validity and large sample properties of emulators, and developing computational algorithms to handle very large data volumes. This work will be validated through proof-of-concept studies with TELEMAC model simulations, forced by realistic patterns of extreme rainfall events and storm surges and located in an urban area of the US. Open-source tools will be generated based on the results from this project and shared with the engineering communities, especially civil infrastructure community that frequently faces challenges in coping with spatially-dependent data and uncertainties.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
