Possible hydrologic forecasting improvements resulting from advancements in precipitation estimation and forecasting for a real-time flood forecast system in the Ohio River Valley, USA

•NEXRAD radar based precipitation estimation has improved dramatically over the Ohio River Valley 1997-present.•Goodness-of-fit statistics show historical hydrologic simulation for a representative watershed illustrates significantly improved precipitation estimation.•Quantitative Precipitation Forecast (QPF) estimation over the period of record has improved marginally.•Based on hydrologic monte carlo simulation experiments, QPF errors are shown to be very significant and resulting hydrologic predictions are highly variable at both past and current QPF accuracy levels.•Results demonstrate the need for the use of ensemble hydrologic prediction methodologies for real-time hydrologic forecasting. Errors in hydrometeorological forcings for hydrologic modeling lead to considerable prediction uncertainty of hydrologic variables. Analyses of Quantitative Precipitation Estimate (QPE) and Quantitative Precipitation Forecast (QPF) errors over the Ohio River Valley were made to quantify QPE and QPF errors and identify hydrologic impacts of forcing errors and possible improvements resulting from advancements in precipitation estimation and forecasting. Monthly, seasonal, and annual bias analyses of Ohio River Forecast Center (OHRFC) NEXt-generation RADar (NEXRAD) based Stage III and Multisensor Precipitation Estimator (MPE) precipitation estimates, for the period 1997-2016, were made with respect to Parameter-elevation Regressions on Independent Slopes Model (PRISM) precipitation estimates. Verification of QPF from NWS River Forecast Centers from the NOAA/NWS National Precipitation Verification Unit (NPVU) was compared to QPF verification measures from several numerical weather prediction models and the NOAA/NWS Weather Prediction Center (WPC). Improvements in NEXRAD based QPE over the OHRFC area have been dramatic from 1997 to present. However, from the perspective of meeting hydrologic forecasting needs, QPF shows marginal improvement. A hydrologic simulation experiment illustrates the sensitivity of hydrologic forecasts to QPF errors indicated by QPF Threat Score (TS) values. A monte carlo experiment shows there can be considerable hydrologic forecast error associated with QPF at expected WPC TS levels and, importantly, that higher TS values do not necessarily translate into improved hydrologic forecasts. These results have significant implications for real-time hydrologic forecasting. First, experimental results demonstrate the value gained in terms of improvement