Merging multi-source precipitation products or merging their simulated hydrological flows to improve streamflow simulation

Abstract

Satellite and reanalysis precipitation products are widely utilized for streamflow simulation, which is one critical hydrological application, especially for ungauged regions. Possible ways to improve streamflow simulation are investigated in this study by merging multi-source precipitation products, or directly merging streamflow simulated with different precipitation products. Two satellite-based precipitation products, Tropical Rainfall Measuring Mission (3B42 Version 7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and one reanalysis precipitation product, National Centers for Environment Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) are selected. Bayesian model averaging (BMA) is used to merge multi-source precipitation estimates and streamflow simulations. The results show that merging multi-source precipitation products made little difference to improve streamflow simulation. Merging multi-source streamflow simulations using the BMA generally achieved better performance on streamflow simulation, indicating that this approach is more efficient than merging multi-source precipitation products.