Impact of sampling of GPM orbital data on streamflow simulations

Abstract

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