Guoqiang Tang is a Professor at the School of Water Resources and Hydropower Engineering, Wuhan University. Before joining Wuhan University, he held research appointments at the University of Saskatchewan and the U.S. National Center for Atmospheric Research (NCAR). He is a recipient of the National Natural Science Foundation of China (NSFC) Excellent Young Scientists Fund (Overseas) and has been listed among the Stanford/Elsevier Top 2% Scientists. His research lies at the intersection of hydrology, remote sensing, and artificial intelligence. He has led the development of multiple continental- and global-scale meteorological products and integrated hydrologic modeling frameworks. Prof. Tang has published over 80 peer-reviewed papers in leading international journals, accumulating more than 6,000 citations. He serves as an Associate Editor of Journal of Hydrology and Frontiers in Water, and has contributed extensively as an editor and reviewer for numerous leading international journals, as well as a convener of multiple international scientific meetings.
Uncertainty in hydrologic modeling is fundamentally driven by the representation of meteorological forcings, yet its origins and propagation pathways at the global scale remain insufficiently understood. This lecture presents a systematic research progression that advances global hydrologic uncertainty analysis through successive innovations in meteorological dataset development. It first examines approaches for constructing temporally continuous and observation-consistent meteorological fields using various approaches (including machine learning techniques), highlighting the role of serial completeness in reducing artificial discontinuities and structural biases in meteorological forcings. It then introduces a transition toward ensemble-based meteorological representations, in which forcing uncertainty is explicitly characterized through probabilistic realizations rather than single deterministic inputs, thereby improving the representation of extremes and low-probability events.
Building on these developments, it further explores how different dimensions of meteorological uncertainty propagate into hydrologic simulations across regional to global scales. The results reveal distinct patterns of uncertainty amplification, attenuation, and spatial heterogeneity, with pronounced sensitivity to climate regimes, topographic complexity, and hydrologic process representation. By linking fundamental dataset production strategies with global hydrologic uncertainty behavior, this work provides a unified scientific framework for interpreting uncertainty propagation in large-scale hydrologic modeling, with implications for climate change assessment, water resources management, and Earth system prediction. More broadly, these findings underscore that uncertainty is not merely a byproduct of modeling limitations, but a fundamental component that should be explicitly quantified and interpreted in hydrologic research.