Performance Evaluation and Calibration of GPM-IMERG Satellite-Based Rainfall Data in Soligi Village
DOI:
https://doi.org/10.51988/jtsc.v7i3.501Keywords:
evaluation, rainfall, GPM-IMERG, calibration, RAPSAbstract
Rainfall is a primary parameter that plays a crucial role in hydrological planning and water resources management. However, several challenges exist in obtaining rainfall data that accurately represent field conditions, including the limited number of rain gauge stations and uneven spatial distribution of data, particularly in developing areas such as Soligi, North Maluku Province. An alternative solution is the use of satellite-based rainfall data such as GPM-IMERG. However, its application in hydrological analysis requires evaluation and calibration to improve its accuracy and reliability. Therefore, this study aims to evaluate and calibrate GPM-IMERG satellite rainfall data against observed rainfall data using statistical parameters: the correlation coefficient (r), bias, Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE). The results indicate that the observed rainfall data are consistent based on the RAPS Method. Calibration of satellite rainfall data against observed rainfall data significantly improves rainfall estimation performance compared to uncalibrated satellite data. During the validation stage, the exponential regression model demonstrated the best performance, with the lowest RMSE value of 96.940 mm and the highest NSE value of 0.770. Therefore, the exponential regression model is considered the most representative in describing the relationship between GPM-IMERG satellite rainfall data and observed rainfall data.
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