<![CDATA[Reliable flood-relevant streamflow forecasting remains difficult at operational scale because physically based forecasting systems provide national coverage but cannot be directly verified at the overwhelming majority of ungauged river reaches. This study reconstructs and strengthens a hybrid neural–physics framework in which the National Water Model (NWM) supplies the physically constrained baseline forecast and an attribute-conditioned deep neural network learns forecast-error magnitude from gauged basins, then transfers that knowledge to ungauged basins. The emphasis is not on replacing process-based hydrology with a black-box predictor, but on improving forecast reliability under cross-event and cross-basin generalization. The empirical setting consists of 979 archived daily long-range forecasts, 30 forecast lead days, 16-member NWM ensembles, and 389 quality-controlled U.S. Geological Survey gauging stations across Alabama and Georgia, complemented by static watershed descriptors and gridded soil-moisture diagnostics. Exploratory analysis shows that forecast skill is strongly conditioned by basin properties: larger and forested watersheds are predicted more accurately than smaller and urban watersheds, while urban basins show severe positive bias, weaker anomaly correlation, and degraded hydrograph timing. These diagnostics motivate an error-learning postprocessor that maps NWM forecast magnitude and basin attributes to a non-negative error estimate and converts the deterministic physics forecast into a corrected predictive interval. Under a temporally disjoint evaluation intended to test cross-event transfer, observation coverage increases from 0.21±0.01 for the raw NWM ensemble range to 0.82±0.03 for the hybrid model; a repeated spatial holdout yields comparable performance (0.82±0.01). The gains are largest where the underlying physics model is weakest, especially in developed watersheds. The main limitation is that improved reliability is achieved through wider predictive intervals rather than sharper hydrographs. The study therefore supports a defensible conclusion of hybrid neural–physics postprocessing can materially improve flood-relevant streamflow reliability at ungauged basins, but interval sharpness and external cross-region validation remain open research needs.]]>
