Diagnosing the Added Value of Remote Sensing and Gridded Precipitation for Daily Runoff Forecasting Under Strong Antecedent Runoff Control

Reliable daily runoff forecasting supports flood risk mitigation and sustainable basin water management, but the added value of external information is difficult to identify when antecedent runoff strongly constrains prediction. This study develops a diagnostic framework to examine whether multi-source remote sensing and gridded precipitation provide additional value beyond runoff memory. The framework was applied to 1-, 3-, 5-, and 7-day runoff forecasting in the Beidao and Baijiachuan catchments of the Yellow River Basin. An external-forcing reference model (M1) used meteorological–remote sensing variables, precipitation statistics, and static catchment attributes, whereas a runoff memory reference model (M2) used only antecedent runoff features. Their validation-based combination formed a fusion benchmark. A residual correction model based on an SRCNN–Transformer architecture (M3) was then used to examine whether the remaining fusion errors could be corrected using gridded precipitation fields and multi-source temporal states. Results show that runoff memory dominated short-lead forecasts but weakened with lead time, while external forcing became more useful at medium and longer leads. M3 produced positive but lead time-dependent Nash–Sutcliffe efficiency gains, with the most stable improvements at 3–5 days. These results describe the potential value of remote sensing and gridded precipitation under known forcing conditions rather than operational forecast skill. These findings provide evidence from two contrasting Yellow River catchments, while their broader applicability remains to be tested under more diverse hydrological and operational forecasting conditions.