Our research focuses on advancing fundamental understanding of the interactions between hydrology and other subsystems using high performance physically-based models. 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 main tool is the Process-based Adaptive Watershed Simulator (PAWS), a comprehensive, computationally-efficient 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 microbiology, biogeochemistry, and human dimensions into a uniform modeling framework, to investigate their mutual interactions.
In parallel, we are interested in understanding the seasonal and inter-annual water balance using remote-sensed data and conceptual frameworks.