Research at HRC involves designing, improving, and implementing prediction systems. For example, HRC’s hydrologic models are used for real-time flash flood warning, channel and river discharge prediction out to 90 days, as well as future climate simulations spanning decades. Prediction of a given field requires uncertainty estimates to instill confidence and meaning. HRC evaluates estimates with remotely sensed data (radar and satellite) and in-situ data. Statistical and dynamical methods are developed and used for downscaling and full dynamical models are adapted and employed in estimation, sensitivity studies, and validation studies.

Improvements in accuracy, efficiency, applicability, and utility of HRC’s forecast models have come largely through research on uncertainty analysis. Research and uncertainty analysis on raw data, data collection schemes, blended combinations of data types, and dynamical and statistical interpolation leads to improved field estimates. Overall model accuracy is improved by identifying and understanding uncertainty in each component, parameter, and input. For instance, research at HRC has identified stable scaling relationships between ensemble flow prediction variability and catchment area that are useful for quantifying prediction uncertainty in small subgauged or ungauged scales. The impacts of model structure and scale are also examined. Uncertainty analysis, model updating and data assimilation are explored in the context of state estimation theory. Research with multi-model ensembles is also used to improve hydrologic model reliability.

Selected Articles and Publications can be seen on the PUBLICATIONS page.