Dr. Kristie Franz – Mentor Department of Geological and Atmospheric Sciences, Iowa State University
The objective of this study was to explore flash flood forecasting by looking at a comparison of streamflow discharge forecasts produced by the lumped Sacramento Soil Moisture Accounting Model (SAC-SMA) using quantitative precipitation forecasts (QPF) as input. The experimental High-Resolution Rapid Refresh Ensemble (HRRRE) and the operationally used High-Resolution Ensemble Forecast (HREF) were tested for three Iowa watersheds during the warm season. Past studies have found that warm season events pose the greatest uncertainty for rainfall prediction, which contributes to uncertainty in streamflow prediction. Precipitation ensembles help to cover the spread of uncertainty and provide value to hydrologic forecasts through the use of random perturbations to their input characteristics and boundary conditions. Datasets for the HRRRE and HREF were collected and evaluated, then processed to yield basin average QPF over the selected watersheds. The QPF ensembles were then fed into the SAC-SMA hydrologic model along with interpolated potential evapotranspiration (ET) and temperature, following a model spin up that ran from 2016 up to the time of the event. For the three watersheds and seven events stretching from the 20th of June to the 5th of September, the HREF outperformed its experimental counterpart. The forecasts produced using the operational HREF outperformed those produced using the experimental HRRRE for evaluations of peak discharge forecasts for both the full distribution of ensemble members and the ensemble mean. This is seen in lower biases between the mean discharge and the observed. Furthermore, the HREF had a lower ranked probability score than the HRRRE when evaluated for peak discharge. The HRRRE-based forecasts were more accurate for prediction of peak discharge timing than the HREF-based forecasts.
Kyle K. Hugeback
Hugeback, Kyle K., "A Comparison of HREF and HRRRE Predictions for Ensemble Flash Flood Forecasting" (2018). Meteorology Senior Theses. 45.