Hongli Liu

Research Seminar

Advancing large-domain process-based hydrologic modeling and prediction

Hongli Liu

Monday, May 6th

NWC 1313 / 3:00 PM

Abstract: Water cycle is the vehicle that delivers meteorological impacts to the land. Hydrologic observations and models underpin the understanding and management of water resources. My research works to advance large-domain process-based hydrologic simulation and prediction. In this seminar, I will present three main contributions that I have made in areas of data uncertainty quantification, spatial configuration, and parameter estimation. In data uncertainty quantification, my research estimates uncertainty in meteorological and hydrometric observations, and also develops methods for using these uncertain hydrometeorological data in parameter estimation and data assimilation of hydrologic modeling and forecasting. In spatial configuration, my research incorporates meteorological conditions into the spatial discretization of hydrologic response units, effectively improving hydrologic model performance. In parameter estimation, my research exploits machine learning methods and develops efficient sensitivity analysis and parameter estimations methods for large-domain hydrologic models. The above research provides reliable hydrologic datasets and innovative mathematical methods that advance large-domain processbased hydrologic modeling and prediction.