Implementing and Evaluating National Water Model Ensemble Streamflow Predictions Using Postprocessed Precipitation Forecasts Journal of Hydrometeorology

Improving probabilistic streamflow forecasts is critical for a multitude of water-oriented applications. Errors in water forecasts arise from several sources, one of which is the driving meteorology. Meteorological forecasts are often statistically postprocessed before being input into hydrologic models. Shifts toward ensemble weather prediction systems have propelled advances in ensemble postprocessing, providing an opportunity to enhance probabilistic water forecasting. This study’s purpose is to implement and evaluate the impact of coupling state-of-the-art precipitation ensemble postprocessing techniques with the process-based, spatially distributed National Water Model (NWM). The postprocessing has two steps: First, precipitation is calibrated using a censored, shifted, gamma distribution approach, and second, it is reordered using an ensemble copula coupling technique. The NWM focuses on flood forecasting but to date has only been run with time-lagged ensemble weather forecasts. We implement the NWM in a medium-range (∼7 day) ensemble forecasting mode for several rain-dominated catchments in Northern California during an extremely wet water year, when advanced warning of heavy precipitation and streamflow could have been useful. Postprocessing enhances NWM streamflow forecasts in terms of ensemble spread and accuracy, improving underestimation. Precipitation (streamflow) was generally skillful out to day 4 (7), including heavy precipitation (>75 mm) and relatively high-flow thresholds, but less consistently for the most extreme streamflow. These results suggest that NWM ensembles could be warranted for priority basins with relatively predictable weather phenomena, though there are trade-offs with hydrological model complexity and ensemble forecasting. This study can inform the NOAA-led Next Generation Water Resources Modeling Framework, which will need to consider how to integrate meteorological postprocessing and ensemble techniques. Grant no. NA17OAR4320101