The primary goal is to develop an automated calibration platform for CONUS-wide watershed calibrations. This platform will allow for the quantification of the different sources of uncertainty on streamflow forecast skill.
Skill in model-based hydrologic forecasting depends on the ability to estimate the initial moisture and energy conditions of a watershed, to forecast future weather and climate inputs, and on the quality of the hydrologic model's representation of watershed processes. The impact of these factors on prediction skill varies regionally, seasonally, and by model. We are investigating these influences using a watershed simulation domain that spans the continental US (CONUS), encompassing a broad range of hydroclimatic variation, and that uses the current simulation models of National Weather Service (NWS) streamflow forecasting operations.