Research

My research asks how complementary observations and model structures can improve our understanding and simulation of water systems across space and time.

Research themes

Data-driven and physics-informed hydrological modeling

To improve simulation and parameter estimation, I combine hydrological process models with differentiable programming and machine learning.

Data assimilation and uncertainty-aware analysis

I use data assimilation and statistical analysis to identify observation and model bias, constrain states and parameters, and characterize uncertainty in estimates of soil moisture, groundwater, water storage, and streamflow.

Multi-scale water-system analysis

I investigate how soil-water, surface-water, and groundwater information can be transferred across scales, using scale conversion and pedo-transfer functions to address differences in process and parameter representation.

Remote sensing and human impacts

I explore how SWOT and other hydrometeorological observations can constrain water-system dynamics and support models that account for human influences, including reservoir operations.

Methods and data

Statistical decomposition, geospatial analysis, and multi-source integration connect these themes. Data sources include SWOT, SMAP, GRACE, ISMN, CAMELS, GLDAS, MODIS, NOAA products, and groundwater observations.

Research Vision

Explore my Research Vision: a future scientific cycle connecting observations, discovery, process understanding, modeling, and diagnosis. Its interactive concept illustrates how observations could inform water-state estimates and how remaining uncertainties could guide future measurements.