Impact of sampling of GPM orbital data on streamflow simulations

• Highlights the sampling effects of GPM orbital through hydrologic modeling. A multidimensional error model is used to generate synthetic data. The VIC scrutinize impact data sampling. An insight into optimum integration time number satellites in a constellation. Global precipitation estimation depends on constellations mainly passive microwave sensors along with satellite radars and geosynchronous earth orbit infrared sensors. With constellations, persisting challenge limited sampling, which impacts applications such as hydrological This study presents framework examine Precipitation Measurement (GPM) streamflow simulations. Orbital from Dual Radar (DPR) 2A-Ku within Variable Infiltration Capacity (VIC) derive ensembles runoff Ensembles fields are generated at different intervals representative products Satellite Rainfall Error Model (SREM-2D) over Hirakud catchment Indian subcontinent period two years (2015–2016). NOAA-NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based gridded rainfall product considered reference for our study. Sampling uncertainty shown amplify statistical analysis indicates that increasing rates tend more realistic land surface fluxes. results indicate simulations using forcing 6-hourly outperforms compared 3-hourly 12-hourly times. exemplified by indices, mean relative 0.17 forcing, 0.12 simulation 0.57 forcing. Root-Mean-Square-Error (RMSE) 3-hourly, 6-hourly, 2015 0.31, 0.25, 1.43 2016 0.15, 1.06, respectively. It can be noticed both RMSE show similar pattern years. generic therefore proposed manifestation constellation simulations, crucial future missions CubeSat-based Temporal Experiment Storms Tropical Systems, Time-Resolved Observations structure storm intensity smallsats.