Research

Our research seeks to understand and predict interactions among land, water, atmosphere, and human systems across timescales ranging from weather to climate. We develop and apply observations, land-surface models, Earth-system models, and artificial intelligence to improve prediction of hydrologic extremes, water resources, and environmental change.

Research Themes

• Land-atmosphere interactions: Understanding how soil moisture, vegetation, snow, groundwater, and surface fluxes influence weather, climate, and hydroclimate extremes.
• Hydrology and water resources: Improving prediction of droughts, floods, streamflow, reservoir evaporation, and water availability.
• Land surface modeling: Developing and applying models such as Noah-MP and CLM to improve representation of water, energy, carbon, vegetation, snow, and groundwater processes.
• Earth system predictability: Investigating sources of predictability from subseasonal to annual timescales, with emphasis on land memory and coupled land-atmosphere processes.
• Extreme events: Studying droughts, floods, heat waves, freezing rain, and compound hazards affecting communities, infrastructure, and water systems.
• AI for Earth system science: Using machine learning and physics-guided AI to diagnose model errors, improve prediction, and integrate observations with process-based models.
• Infrastructure resilience and climate risk: Connecting environmental prediction with energy, water, and infrastructure resilience planning.

Selected Current Directions

• NASA-supported research on water resources, land modeling, and prediction.
• DOE GRID-AWARE research on compound hazards, Earth-system predictability, and infrastructure resilience.
• Texas water-resources studies involving streamflow, reservoir evaporation, drought, and hydrologic extremes.
• Freezing-rain and winter-weather hazards affecting infrastructure systems.