Our group conducts research at the intersection of land-atmosphere interactions, hydrology, Earth-system prediction, water resources, artificial intelligence, and infrastructure resilience.
We develop and apply land-surface models, hydrologic models, NASA Earth observations, and land-data-assimilation products to improve understanding and prediction of terrestrial water-cycle processes, including soil moisture, evapotranspiration, runoff, streamflow, and drought.
GRID-AWARE focuses on Gulf-regional infrastructure resilience, Earth-system predictability, and AI-enhanced prediction of weather-related hazards affecting energy, water, and infrastructure systems. The project integrates environmental prediction, land-atmosphere interactions, compound hazards, and stakeholder-informed resilience applications.
Our group studies streamflow, reservoir evaporation, drought, hydrologic extremes, and water-resource predictability in Texas and beyond. This work supports improved understanding of regional water availability and decision-relevant hydrologic information.
We investigate freezing-rain and winter-weather hazards, with emphasis on regional prediction, infrastructure impacts, and risk to power, transportation, and urban systems.
We contribute to the development, diagnosis, and application of Noah-MP and related land-surface models. Our work emphasizes representation of vegetation, soil moisture, groundwater, snow, runoff, evapotranspiration, and land-atmosphere coupling.
We explore artificial intelligence and machine learning methods for model diagnosis, prediction, uncertainty quantification, and integration of observations with process-based Earth-system models.