Satellite-based precipitation products are widely used for hydrological modelling; however, temporal sampling uncertainty caused by infrequent satellite overpasses can significantly affect runoff estimation, particularly in mountainous and flood-prone watersheds. This study evaluates the influence of temporal sampling uncertainty in precipitation on runoff simulation using multi-sensor combination data from two different satellite constellations (Global Precipitation Measuring Mission-GPM and Time Resolved Observations of Precipitation Structure and Storm Intensity with a Constellation of Smallsats - TROPICS) constellations over the Ranikhola watershed in the eastern Himalayas, India. Five GPM sensors (GMI, SSMIS, MHS, AMSR2, and ATMS) and three TROPICS CubeSats (T3, T5, and T6) were analysed individually and in different combinations. Sensor overpass times were collocated with IMERG precipitation data, and the derived rainfall was used to simulate runoff in the HYSIM (lumped model) hydrological model during 2016–2020. Results show that increasing the number of sensors improved sampling frequency, reduced revisit gaps, and enhanced runoff simulation accuracy. The combined TROPICS configuration (T3–T5–T6) achieved the best performance, with NSE = 0.6983, R2 = 0.9568, RMSE = 7.75 m3 s−1, and MAE = 5.62 m s−1, whereas the GMI–SSMIS combination showed poor performance with NSE = −1.7385 and RMSE = 23.36 m3 s−1. The TROPICS constellation provided more than 12 overpasses per day, compared to ∼1–2.5 overpasses per day for individual GPM sensors. The findings demonstrate that high-frequency CubeSat constellations substantially reduce temporal sampling uncertainty and improve runoff estimation, highlighting their potential for flood forecasting and hydrological applications in data-scarce mountainous regions.