Hi, I’m Peijun Li
Previous Work:
- Efficient parameter estimation and hybrid modeling
- Differentiable parameter learning
- Data and model bias identification and correction
- Cross-scale, multi-source data integration and modeling
- Coupled surface water–soil moisture–groundwater systems
I earned my Ph.D. in Hydrological Sciences and am currently a Postdoctoral Scholar at Penn State, where I conduct research with the MHPI team.
My research focuses on modeling and analyzing hydrological processes and their spatiotemporal variability. I employ a range of approaches, including statistical techniques, physically-based models, and hybrid methods that integrate both. These approaches are applied to diverse data sources, such as remote sensing products, in-situ observation networks—particularly sites equipped with specialized sensors—and secondary datasets.
Recently, I have been working on improving hydrological modeling using SWOT data and incorporating human activities and reservoir operations into models.
Prospects
- Multi-scale spatio-temporal analysis and simulation of hydrological processes
- Data assimilation and integration of multi-source hydrometeorological data
- Synergistic integration of data-driven and physically-based hydrological models
- Development of hydrological models incorporating anthropogenic influence
Quick Look
News
News about my studies:
- Chinese report from State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China