Research

My research combines hydrological process understanding with statistical, machine-learning, and physically based modeling. I work with remote-sensing products, in-situ observations, and secondary datasets to study how hydrological processes vary across space and time.

Research themes

Data-driven and physics-informed hydrological modeling

Developing hybrid approaches that combine process-based models with differentiable programming and machine-learning methods for improved simulation and parameter estimation.

Data assimilation and uncertainty-aware analysis

Using observations and model structure together to identify bias, constrain model states and parameters, and improve estimates of soil moisture, groundwater, terrestrial water storage, and streamflow.

Multi-scale water-system analysis

Connecting soil water, surface water, and groundwater information across spatial and temporal scales, including scale conversion and pedo-transfer functions.

Remote sensing and human impacts

Using SWOT and other hydrometeorological datasets to investigate water-system dynamics, while accounting for anthropogenic influences such as reservoir operations.

Methods and data

My work uses hydrological modeling, statistical decomposition, data assimilation, differentiable modeling, machine learning, geospatial analysis, and multi-source data integration. Relevant data sources include SWOT, SMAP, GRACE, ISMN, CAMELS, GLDAS, MODIS, NOAA, and groundwater observations.

Current directions

Current directions include multi-scale hydrological simulation, integration of multi-source observations, synergistic data-driven and physically based models, and models that represent human influence on hydrological processes.