Research Interests

Our research focuses on advancing fundamental understanding of the interactions between hydrology and other subsystems (e.g., ecosystem, energy and carbon cycles, sediment and channels).

Our primary methods include (1) high performance physically-based hydrologic models; and (2) state-of-the-art machine learning methods. The former allows us to conduct experiments, while the latter, through mining land-based and remotely-sensed data, help generate hypotheses about how the system functions, e.g., in our recent paper on the processes controlling storage-streamflow correlations.

We constantly look at improving hydrologic descriptions, integrating novel processes and enhancing predictive capabilities to meet the challenge of global change. Currently, we use modeling to evaluate water resources sustainability in water-energy nexus. Our research trespasses Large-scale computational hydrology, Water-carbon-nutrient interactions, Scale issues, Remote sensing hydroinformatics, River hydrology-hydraulics interactions.

One tool we use intensively is the Process-based Adaptive Watershed Simulator (PAWS), a comprehensive, computationally-efficient parallel hydrologic model designed for large-scale simulation. The model is now coupled to the Community Land Model (CLM), and therefore is able to simulate Carbon/Nitrogen cycling, ecosystem dynamics and their interactions with the water cycle. Due to its comprehensiveness, efficiency and flexibility, this tool provides an excellent platform for the integration of biogeochemistry, fluid mechanics, and human dimensions into a uniform modeling framework, to investigate their mutual interactions.

Shen's Google scholar profile


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