Logo image
Evaluation of Short-Range Streamflow Forecasts at Flash-Flood Prone Basins with the National Water Model
Journal article   Peer reviewed

Evaluation of Short-Range Streamflow Forecasts at Flash-Flood Prone Basins with the National Water Model

Felipe Quintero, Witold F. Krajewski and Humberto Vergara
Journal of hydrometeorology, Vol.27(7), pp.1147-1161
07/2026
DOI: 10.1175/JHM-D-26-0026.1

View Online

Abstract

We present a verification analysis of the operational short-range streamflow forecasts produced by Office of Water Prediction with the National Water Model (NWM) from 2019 to 2024. Forecasts were compared to hourly observations at 7,500 USGS locations over the conterminous United States (CONUS). The study has two goals: 1) estimate the probability of detection (POD) of high flow events, focusing on small basins prone to occurrence of flash floods; 2) quantify the skill that can be attributed to the model structure, the streamflow data assimilation, and the quantitative precipitation forecasts from the High-Resolution Rapid Refresh (HRRR) model using the Kling Gupta Efficiency (KGE) score. When the model uses streamflow data assimilation, the median POD reduces from 0.8 at the first hour of the forecast to zero after seven hours. When there is no streamflow data assimilation, the median POD reduces from 0.1 at the first hour of the forecast to zero after five hours. When examining the individual components of the forecasting system, it was found that the errors in precipitation forecast reduce KGE at headwater basins. Our results suggest that the main factor limiting the skill to predict high flow events at small basins are states of the model that are used to initialize the forecasts, especially when the model has no access to streamflow assimilation. Under these conditions, the forecasts have a median KGE value of -0.6, which is below the KGE acceptable reference value of −0.41.
Forecast verification/skill Operational forecasting Short-range prediction Hydrologic models Model evaluation/performance

Details

Logo image