The rainfall-runoff transformation occurring on the land-surface is a key determinant of the watershed response to climate change, land-use variations and human impacts. Our group investigates the physical controls and mechanisms involved in the rainfall-runoff transformation in small to large watersheds using numerical modeling, remote sensing data and field instrumentation. In particular, we seek to capture and represent the spatial and temporal distribution of surface and subsurface hydrological processes to gain an understanding of watershed non-linearity, scale effects and modeling uncertainty. The spatial distribution of basin characteristics and hydrometeorological forcing leads to gradients in the surface hydrologic and energetic response which can be captured in high resolution models via boundary conditions, initial conditions and assimilation techniques. We support code development and enhancements of a fine-resolution model for watershed investigations.  
     
 
     
 
  Our group utilizes and actively develops the TIN-based Real-Time Integrated Basin Simulator (tRIBS) to simulate of hydrological processes over regional watersheds. The tRIBS model is a physically-based, fully-distributed hydrologic model based on a triangulated irregular network (TIN). It has been developed for simulation of watershed hydrology using rainfall estimates from gauges, weather radar or numerical weather models. Over large watersheds, the coupled surface and groundwater response to rainfall is modeled by tracking infiltration fronts, water table fluctuations and lateral moisture exchanges. Surface runoff is generated via four mechanisms, infiltration-excess, saturation-excess, perched return flow and groundwater exfiltration, and routing of surface flow is achieved via overland flow and channel routing. Evaporation from bare soil, vegetation and intercepted rainfall is computed via a surface radiation and energy balance. The tRIBS model is capable of simulating basin hydrology while preserving data from remote sensing and field measurements.  
     
 
  Data assimilation is a framework allowing the systematic merging of physical models and measurements under the supposition that both are imperfect, but provide useful information about the distributed state. For example, the combination of field data obtained from campaigns over many locations in a watershed and a physical model of the watershed hydrology. In data assimilation, the physical processes responsible for the evolution of the system are represented by the forward model (e.g. tRIBS), Our group is investigating the use of remote sensing and in-situ observations in conjunction with a physical model to estimate distributed hydrologic states and their temporal evolution. In addition, we attempt to estimate the model uncertainty by accounting for the effects of model input and measurement errors and their propagation.  
     
 
     
 
Hydrological processes in the tRIBS distributed model over complex terrain
 
 
     
Distributed watershed observations during field campaign and watershed physical model.