Seminar Hongli Liu "Improving data uncertainty handling in hydrologic modeling and forecasting applications"

Seminar "Improving data uncertainty handling in hydrologic modeling and forecasting applications"

Hongli Liu is an assistant professor at the University of Alberta (UA). Prior to joining UA, Hongli obtained her PhD degree in Civil Engineering from the University of Waterloo and worked as a postdoctoral fellow at the National Center for Atmospheric Research (NCAR) in Colorado and the University of Saskatchewan in Alberta. Hongli’s research focuses on hydrologic modeling and forecasting applications.

• Thursday, June 15, 2023, 4:15 pm (Santiago-CL time), 2:15 pm (AB time)
• Online
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Seminar abstract

In hydrologic modeling, we often need to deal with data uncertainty, for example, meteorological and streamflow data uncertainty. The common way of handling data uncertainty is perturbing observation data with assumed statistical error models (e.g., addictive or multiplicative Gaussian error model). With the advent of advanced meteorological and streamflow uncertainty estimation methods and products, we call for the replacement of assumed statistical error models with existing ensemble data products in hydrologic modeling. In this seminar, I will introduce an ensemble dressing method to estimate the uncertainty of deterministic meteorological data, and then introduce methods for using ensemble data in hydrologic model parameter estimation and data assimilation.

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