Abstract
The integration of the availability and processing of The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data by the Google Earth Engine (GEE) platform is used in this study to extract the estimated monthly rainfall in South Sulawesi. Several areas are selected based on the characteristics of the rainy period cycle representing South Sulawesi, namely Makassar, Masamba, Wajo, and Bone. Monthly rainfall estimation data of CHIRPS in the year 2019 were validated by monthly observed rainfall at the same period showing the CHIRPS rainfall estimation has not been maximized with correlation coefficient values are 0.94, 0.63, 0.65, 0.75, and RMSE percentage 54%, 52%, 95%, 64% for each of the study areas. Then the increase in rainfall estimation performance is carried out by applying multiple linear regression method and considering both monthly observed and estimated rainfall during 30 years from 1989 to 2018, latitude and longitude point as well as elevation in every location. The results show an increase of correlation coefficient to 0.95, 0.74, 0.74, and 0.87 and a general decrease of RMSE percentage to 53%, 39%, 80%, and 67%. Thus, monthly rainfall estimation performance improvement is successfully achieved in various rainy period cycles of the study area.
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