This study evaluates the hydrological performance of the virtual gauge-based method (VG) for flood forecasting in basins devoid of precipitation stations. The Xiaoergou basin was selected as the study area, with flood-season data (2010–2019) including rainfall observations from adjacent basins, outlet streamflow records, and six precipitation products. The VG method divides the basin into multiple sub-regions based on the actual locations and density of the gauges, and then determines virtual gauge locations and precipitation using multi-source precipitation products, and integrates virtual with actual gauges to estimate rainfall fields and areal rainfall. As a control, the Thiessen polygon method was applied to estimate rainfall fields and areal rainfall from the observed rainfall. VG- and Thiessen-derived areal rainfall were used as inputs to hydrological model, with flood forecasting accuracy evaluated through multiple metrics. Results demonstrate that the VG method can adaptively adjust the number, location and rainfall of virtual gauges according to the spatial characteristic of rainfall fields, and dynamically optimizing the weighting of precipitation products. Compared with the Thiessen polygon method, VG-driven hydrological simulations achieved up to approximately 50% higher flood volume prediction accuracy and a notable increase in flood event pass rates. Consequently, the VG method demonstrates superior rainfall estimates and flood simulation capabilities in regions with no precipitation stations.
Dou et al. (Wed,) studied this question.