A Novel Framework for Correcting Satellite-Based Precipitation Products for Watersheds with Discontinuous Observed Data, Case Study in Mekong River Basin

Satellite-based precipitation (SP) data are gaining scientific interest due to their advantage in producing high-resolution products with quasi-global coverage. However, since the major reliance of is on distinctive geographical features each location, they remain at a considerable distance from station-based data. This paper examines effectiveness convolutional autoencoder (CAE) architecture pixel-by-pixel bias correction SP for Mekong River Basin (MRB). Two satellite-based (TRMM and PERSIANN-CDR) gauge-based product (APHRODITE) gridded rainfall mined this experiment. According estimated statistical criteria, CAE model was effective reducing gap between benchmark both terms spatial temporal correlations. The two corrected (CAE_TRMM CAE_CDR) performed competitively, TRMM appearing have slight over CDR, however, difference minor. study’s findings proved deep learning-based models (here CAE) products. We believe that technique will be feasible alternative delivering an up-to-current reliable dataset MRB studies, given sole available area has been out date long time.