Assessment of Hydrologic Predictions Based on a Mix-and-Match Framework Using Multimodel and Multiprecipitation Forcing Data

The Iowa Flood Center (IFC) has conducted a NOAA Hydrometeorology Testbed (HMT) project called “Assessment of hydrologic forecasts generated using multi-model and multi-precipitation product forcing.” The project enables hydrologic predictions made using multiple distributed hydrologic models driven by multiple high-resolution precipitation forcing products. The project uses a variety of precipitation forcing products including MRMS, IFC (our in-house product), and NWS polarimetric QPE with/without short-range precipitation forecasts to drive the National Water Model (NWM) and the IFC Hillslope-Link Model (HLM). The precipitation forecasts are based on the NOAA’s High Resolution Rapid Refresh system. In this study, we use a mix-and-match approach to assess prediction skills affected by the sources of uncertainty due to different combinations of the precipitation forcing products and distributed hydrologic models. Examples include comparing two different rainfall-runoff routing schemes using the same input, or using different input products for exactly the same hydrologic model configuration. Assessment of the mix-and-match results based on comparisons with streamflow observations leads to better understanding of the overall prediction system requirements for skillful prediction. We perform a multi-year assessment using statewide stream gauges including about 150 USGS streamflow gauges. We consider all possible combinations of the modeling elements (forcing products and model routing components) to evaluate predictive capability affected by each one of them. The analysis framework investigates differences in multiple aspects of streamflow (e.g., peak flow) among different combinations and the behavior of these differences with changes in the upstream drainage network. We discuss forecast errors that are represented as a function of multiple factors such as forecast lead time, basin scale, forcing product uncertainty, and hydrologic model structure and components.