Exploring Seasonal Patterns of the Radar-Rainfall Uncertainty Effects in Streamflow Prediction

Quantitative precipitation estimation (QPE) is critical for testing and operating flood forecasting systems. QPE represents one of the main hydrological model forcings, and its uncertainty strongly impacts forecast performance. Our previous findings indicated that the radar mosaic (used for the QPE construction) strongly influences modeling uncertainties. We also found results suggesting an important seasonal pattern possibly explained by the issues faced by QPEs during the winter and early spring seasons. We explored these seasonal trends further by analyzing their spatial distribution and variability. We ran the Hillslope Link Model (HLM) for Iowa between 2015 and 2020, using the Multi-Radar/Multi-Sensor System (MRMS) and two IFC radar-derived products, i.e., reflectivity-based IFCZ and specific attenuation-based IFCA. We perturbed the products with a multiplicative error term. We assessed the model performance at 112 watersheds with USGS observations. We computed the performance at the observed hydrographs, considering the observed rainfall before and during the event. We calculated each gauge’s logKGE, peak flow, and time-to-peak difference. According to our results, the season and the total rainfall strongly influence the QPE-modeling uncertainties on their magnitude and spatial distribution. We evidenced higher biases during the cold season (January to February) and lower in the warm season. We also observed an intensification of the spatial patterns associated with the radar mosaic during the cold season and a decline in the warm one. Seasonal QPE limitations may have severe implications on streamflow prediction and are likely biasing our conclusions. Our work is a step forward in the intricate task of separating QPE-modeling uncertainty, which we consider highly relevant for the further development of our flood forecasting systems.

I presented the advances on this work in the AWRA spring conference held in Tuscaloosa, Alabama.