Ruben Doria

Agricultural production remains highly susceptible to water-related risks, such as drought and flooding. Although hydrologic forecasting systems, such as the National Water Model (NWM), have advanced considerably, their outputs are rarely used for real-time agricultural decision-making. This study developed the Agricultural Water Risk Indicator (AWRI), a framework that translates NWM streamflow forecasts into crop-specific risk assessment indicators. The AWRI framework has three key components: (1) the hydrological threat and exposure characterization based on NWM streamflow forecasts (B1); (2) crop sensitivity by growth stage and water needs (B2); and (3) adaptive capacity reflecting the presence of irrigation or drainage infrastructure (B3). The AWRI was evaluated across three NWM reach IDs covering five farm sites in the Black Belt region of Alabama, USA. The results show that the AWRI captured variations in hydrologic conditions, risk, and crop tolerance across the research sites within the one- to four-week forecast range. Crops in the reproductive stage were especially sensitive. Without resilience measures, up to 55% of the crops simulated at some sites had high-risk AWRI categories. Including irrigation or drainage decreased risk scores by one to two levels. The AWRI tool provides farmers and stakeholders with critical information to support proactive agricultural water management.

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.