Evaluation and Correction of Satellite-Based Rainfall Products (GPM) in the Upstream Part of Bengawan Solo Basin using MCCV Parameterization

In conservative manner, water managers utilize ground station dataset for hydrology analysis. Due to spatially sparse and unreliable precipitation gauges, many have shifted to the application of remote sensing products or satellite-based precipitation products (SBPPs). One of few notable SBPPs is the Global Precipitation Measurement (GPM), which has shown excellent performances. Despite its significant progress, systematic biases are inevitable in the process of recording precipitation. Consequently, a simple yet effective correction process is necessary to be carried out prior to SBPPs' wide applications. This study proposes a non-complex transformation using Monte Carlo-Cross Validation (MCCV) for its function parameterization. The rainfall products in focus are the monthly GPM 3IMERGDF V06 in the Upper Bengawan Solo Basin, in Central Java. Evaluation using several statistical functions demonstrate that the GPM IMERG is capable in capturing the monthly rainfall, supporting by the mean correlation of 0,76. After the correction process, the GPM IMERG slightly improves its performance, indicated by the reduced error of approximately 16%. This correction procedure also allows for correction of the monthly rainfall overestimation, particularly those exceeding 600 mm/month.