Hydrological Sciences - Distinguished Lecture
Title: Uniting Data Assimilation and Physics-Informed Machine Learning to Confront Ontological Uncertainty in Extreme Events Modeling


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Hamid MORADKHANI

University of Alabama

Speaker Biography

Prof. Hamid Moradkhani is the executive Director of the Center for Complex Hydrosystems Research, the Alton N. Scott Chair of Engineering, and Professor of Hydrology and Water Resource at the University of Alabama. Previously, he was the director of Water Resources and Remote Sensing lab and professor of Civil and environmental engineering at Portland State University. His broad research interests are on scientific, pedagogical, and logistical challenges of coupled atmosphere, fresh water, and land systems. The central emphasis of his research is to build capacity to cope with grand challenges in the twenty-first century where the interdependence of critical resources and the imperative for ensuring sustainability is underscored by the Climate-Water-Energy-Food Nexus. He is pioneer in introducing algorithmic and methodological solutions to variety of hydrosystems problems. He has worked extensively at the complex nexus of extreme events, harnessing data revolution, and hydrosystem management. His scholarly agenda is multi-fold and overlaps disciplines including hydrologic and hydrodynamic modeling, development and application of inverse modeling and ensemble data assimilation, uncertainty quantification, physics-informed machine learning, data analytics and high-performance computing.

He is an elected fellow of the American Geophysical Union for “fundamental contributions to data assimilation, understanding, modeling and prediction of hydroclimate extremes key to enabling the societal resiliency”. Also, he is the recipient of several awards including the Horton lecturer award from American Meteorological Society, Arid Lands Hydraulic Engineering award from the American Society of Civil Engineers, the Outstanding Research and Innovation Award from the American Association of Water Resources Engineers (AAWRE), Branford P. Millar Award, for exceptional scholarship in research, instruction, university and public service. He is a fellow of the American Society of Civil Engineers, Environmental and Water Resources Institute, and the board certified of water resources engineering from AAWRE. He has been the Editor of flagship AGU Water Resources Research and Earth’s Future and on the editorial board of several other journals.


Abstract

Hydrometeorologic extreme events represent a significant socio-economic and infrastructure threat on a global scale, with their associated risks expected to rise due to climate change and human development. These events inflict billions in damages annually, severely impacting human livelihoods and resources. A major factor in ineffective flood management is the incomplete understanding of the nonlinear and complex climatic, hydrological, and hydrodynamic processes driving compound floods. This ontological uncertainty hampers the development of effective tools, methods, and technologies for flood characterization and modeling, underlining the urgent need for a deeper understanding of the primary factors involved, better accounting for uncertainties, and quantification of vulnerabilities and risks. Despite progress in creating dynamic, physically based models to simulate floods, these models often lack the necessary accuracy and reliability for operational use. This shortfall is linked to a limited grasp of the underlying mechanisms of flood processes, the predictability issues, assumptions made during model development, and the inadequate integration of uncertainties. This presentation outlines the principal challenges in this field and proposes a comprehensive framework that incorporates human activities, hydrologic variables, and physical attributes such as topography, river morphology, and land-use. This framework aims to refine our understanding of the genesis of riverine, coastal, and compound floods and to enhance flood forecasting and inundation modeling by integrating cutting-edge process-based models, data assimilation, and machine learning techniques while addressing cascading uncertainties.





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