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  • Presentation | H12B: Advancing Hydrologic Processes to Improve Flood Prediction I Oral
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  • H12B-01: Advancing Continental-Scale Hydrology Model Calibration Using Large-Sample Emulators across a Range of Model Complexity (invited)
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  • Location Icon243-244
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Author(s):
Andrew Wood, National Center for Atmospheric Research (First Author, Presenting Author)
Guoqiang Tang, Wuhan University
Mozhgan Askarzadehfarahani, NSF National Center for Atmospheric Research
Naoki Mizukami, NSF National Center for Atmospheric Research
Sean Swenson, NSF National Center for Atmospheric Research
Chanel Mueller, US Army Corps of Engineers
Chris Frans, US Army Corps of Engineers
Marketa McGuire, Bureau of Reclamation Denver
Brantley Thames, US Army Corps of Engineers


This presentation highlights a new approach for calibrating hydrology models over large domains, leveraging insights and methods from machine learning to push beyond the limits of traditional individual basin model calibration and regionalization. Findings from modeling across a range of complexity and physical realism are presented.



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