Flood Modeling For Risk-Informed Decision-Making Under Deep Uncertainty
Abstract
Data-driven, physics-informed, and hybrid modeling approaches are aimed at providing reliable and communicable information to decision-makers and stakeholders at various levels of emergency management including mitigation, preparedness, response, and recovery. Here, I discuss the importance of characterizing, quantifying, and reduction of various uncertainties involved in coupled Hydrological and Hydrodynamic modeling simulations as well as their complex interactions and cascading effects in forecasting Fluvial, Pluvial, Coastal and Compound Flooding (CF). Over the past few decades, CF has come to attention across the globe as it frequently poses larger economic, societal, and environmental impacts than those from isolated flood hazards. A warming climate along with increased urbanization in flood-prone areas are expected to contribute to an escalation in the risk of CF in the near future. Recent advances in remote sensing, machine learning and data assimilation provide a wide range of possibilities to account for and reduce the predictive uncertainties; hence improving the predictability of compound flood events that enables risk-informed decision-making.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H25B..03M