Impact of GPM Temporal Sampling on Streamflow Simulations

Abstract:

Precipitation sampling uncertainty remains a persisting challenge for the next generation of earth and space science satellite constellations. Understanding sampling uncertainty is key for hydrological and hydrodynamical modeling as the error components of satellite orbital data tend to interact non-linearly with hydrologic modeling uncertainty. In the present study, the Global Precipitation Measurement (GPM) Dual Precipitation Radar (DPR) 2A-Ku orbital data is used with the Variable Infiltration Capacity hydrologic model to derive ensembles of runoff simulations. Synthetic precipitation fields representative of GPM orbital data were generated using a Satellite Rainfall Error Model (SREM-2D) from the GPM DPR 2A-Ku and the NOAA-NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based gridded rainfall product as the reference precipitation. The sensitivity of DPR sampling uncertainty towards simulation of hydrological fluxes was studied by running the VIC model at three different forcing intervals (3, 6, 12 hourly). Ensembles of streamflow simulations were computed using different forcing intervals and compared with in-situ daily streamflow measurements. Results generated over a period of 2 years (2015-2016) over the Hirakud catchment in India show that sampling uncertainty tends to amplify uncertainty in streamflow simulation. Streamflow simulations using precipitation forcing at 6-hourly time interval outperform other simulations using 3-hourly and 12-hourly forcing. This is exemplified by the statistical indices, with a mean relative error of 0.17 for streamflow simulations with 3-hourly forcing, 0.12 for simulation with 6-hourly forcing, and 0.57 for 12-hourly forcing. A generic framework is therefore proposed to study the manifestation of sampling uncertainty in hydrologic simulations, which are crucial for future missions such as CubeSat-based Temporal Experiment for Storms and Tropical Systems, and Time-Resolved Observations of precipitation structure and storm intensity with a constellation of smallsats.