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). Water scarcity and excess create varied conflicts and competitions in different parts of the world, and drastic changes in the water cycle put stress on natural and societal systems. Important but often unnoticed to many is that the changes in water states and flows are a significant driver for changes in other systems, e.g., ecosystem, wildfire regimes, carbon and nutrient cycles. We strive to provide sound physical science, produced by data, data-driven and process-based models, to support decision-making. Meanwhile, our fundamental understanding of the hydrologic cycle, after decades of research, still remains much to be improved. We strive to identify commonalities and underlying principles behind varied phenomena.

Our primary methods include (1) high performance physically-based hydrologic models; and (2) state-of-the-art machine learning. 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. Recently, we have focused on deep learning of soil moisture. Deep learning has been found to be an exceedingly powerful tool.

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 the 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.


Please consider submitting to WRR special issue on “Big Data & Machine Learning in Water Sciences: Recent Progress and Their Use in Advancing Science”

Comments welcome on "Shen, CP., Laloy, E., Albert, A., Chang, F.-J., Elshorbagy, A., Ganguly, S., Hsu, K.-L., Kifer, D., Fang, Z., Fang, K., Li, D., Li, X., and Tsai, W.-P.: HESS Opinions: Deep learning as a promising avenue toward knowledge discovery in water sciences, Hydrol. Earth Syst. Sci. Discuss.,, (2018, open for discussion)."

Shen's Google scholar profile




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