Evaluating the Performance of the National Water Model: A Spatiotemporal Analysis of Streamflow Forecasting

The National Water Model (NWM)’s streamflow forecasts are widely used by stakeholders to make critical water management decisions. This study evaluates the performance of the NWM v2.1 in simulating streamflow across the Alabama Black Belt Region (ABBR), in the southeastern United States. Using retrospective NWM and USGS observed streamflow data, model performance was assessed across four-time scales—hourly, daily, weekly, and monthly—using three metrics: Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error Ratio (RSR), and Percent Bias (PBIAS). The results demonstrate that the NWM accuracy improves significantly with longer-term forecasts. At the monthly scale, 89% of evaluated stations reached above “Good” classification based on NSE (>0.75), and 85% based on RSR (<0.5). However, consistent negative bias was observed across all time scales, particularly in the underestimating flows. The results highlight the influence of environmental factors, including land use, topography, and soil characteristics, on model performance, as well as potential sources of systematic bias within the model’s processes. Although the NWM does not incorporate regulated protocols, its ability to capture flow variability improves at aggregated scales, suggesting its suitability for long-term planning applications. These findings underscore the need for further model structure refinement and regional calibration to enhance predictive reliability.