报告题目：Harnessing Data Revolution in Hydrology: The Pursuit of Predictive Modeling and Satellite Data Assimilation

报告人：Hamid Moradkhani教授，The University of Alabama

邀请人：刘攀 教授

时间：2019年5月31日（星期五）上午10:00

地点：水利水电学院八教8213会议室

报告人简介：

Hamid Moradkhani is the Alton N. Scott endowed chair in civil and environmental engineering and founding Director of Center for Complex Hydrosystems Research at the University of Alabama. Prior to his current positions, he was a professor and director of Water Resources and Remote Sensing Lab in the department of civil and environmental engineering at Portland State University.

His research has focused on advancing the understanding of hydrologic science through modeling climate-water-human interactions as a complex system to result in sustainable management. Also, his research emphasizes harnessing data revolution, predictive science, remote sensing, data assimilation and deep learning. Currently he serves on Canada FloodNet international advisory board, NOAA drought task force, ad hoc technical committees and editorial board of several journals. With over 26 years of professional experience he has been the consultant, advisor and expert witness to numerous national and international organizations. He is a Fellow of the American Society of Civil Engineers, Fellow of Environmental and Water Resources Institute, and Diplomat of Water Resources Engineering, designated by the American Academy of Water Resources Engineers. He was also elected to the hall of fame of Samueli College of Engineering at the University of California, Irvine, received Branford P. Millar Award from Portland State University for exceptional scholarship and research, instruction and public service. Moradkhani obtained his PhD degree from University of California, Irvine in civil and environmental engineering majoring hydrology, water resources and remote sensing.

报告简介：

    A grand challenge for current and future generations is to promote sustainable solutions for hydrosystems. Two key aspects of this view toward water systems resilience and sustainability are: (a) multi-disciplinary research to understand and model the climate-water-ecosystem linkages, identify the communities and ecosystems that are most vulnerable and specify how these ecosystems can best adapt to climate variations and change, (b) cyber-innovation for hydrologic science/engineering with predictive modeling under uncertainty by means of state-of-the-art remote sensing, data assimilation and machine learning. In addition, nonstationarity in hydroclimatic extremes caused by anthropogenic climate warming has increased the likelihood of extreme events which imposes considerable challenges for hydrometeorologists and practitioners in hazard modeling and management. This presentation covers some of our developments in predictive modeling and data assimilation and their applications.

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