Utilization of quantile mapping method using cumulative distribution function (CDF) to calibrated satellite rainfall GSMaP in Majalaya watershed

Abstract

Abstract Flooding is a major problem in Indonesia, with a need for a more data-driven model to understand the sources of floods and potential measures. As ground-based data availability and quality are major sources of uncertainty in flood modeling in such a developing country, satellite-based data is one significant option to complement the drawbacks of ground-based data. The data available from the satellite data still needs to be calibrated with the ground-based observation data. This research uses satellite rainfall data from GSMaP (Global Satellite Mapping and Precipitation) by JAXA and the quantile mapping method to calibrate the satellite rainfall data with local rainfall observation. The quantile method is a bias correction method frequently used for precipitation and temperature variables. This method investigates the bias in regional satellite rainfall data and local rainfall observation. The satellite and ground-based data scales are changed to quantiles or percentiles to calculate the correction factors. The quantile mapping method aims to determine the closely fitted CDF curve between the satellite and the ground-based rainfall data. The study area is Majalaya, one of the regencies in West Java known as the metropolitan city for the textile industry and agriculture center. The Quantile Mapping method successfully calibrated the GSMaP rainfall data in Majalaya with an R2 of 0.996. This research can solve the problem of rainfall data in Indonesia and can further be developed for other purposes, such as flood analysis and prediction.