Water and carbon flux monitoring using eddy-covariance and surface renewal methods
Hydrologic model development, emphasizing parameterization of boundary conditions, evaluation of modelled physical mechanisms, and data-driven validation of model outputs
Interpretation and ground-truthing of remote sensing data, especially ET models
Statistical analysis of hydrologic and ET data, crop water productivity
Machine learning analysis of time-series, specializing in LSTM neural networks and other supervised regression methods
