Assessing Regional Precipitation Patterns Using Multiple Global Satellite-Based Datasets in the Upper Citarum Watershed, Indonesia

• Published in: Journal of the Indian Society of Remote Sensing Vol. 52; no. 10; pp. 2251 - 2265
• Main Authors: Habibie, Muhammad Iqbal, Nurda, Nety, Sencaki, Dionysius Bryan, Putra, Prabu Kresna, Prayogi, Hari, Agustan, Agustan, Sutrisno, Dewayany, Bintoro, Oni Bibin, Yulianto, Swasetyo, Arifandri, Robby
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01-10-2024
• Subjects: Earth and Environmental Science; Earth Sciences; Remote Sensing/Photogrammetry; Review Article; Citarum; Global satellite-based precipitation (GSP); Inverse distance weighting (IDW); Rain gauge; Watershed
• ISSN: 0255-660X; 0974-3006
• DOI: 10.1007/s12524-024-01952-9

High spatial resolution, accurate precipitation data, which is currently unavailable in many parts of the world, is required for a variety of applications. This study investigates the utility of employing Global Satellite Precipitation (GSP) in conjunction with ground-based precipitation data. Monthly studies of the research area were performed using fourteen various GSP models, including CHIRPS, CFS, CPC CMORPH, and others. According to our findings, CHIRPS is the most successful GSP model we investigated. All GSP models performed better when the daily precipitation data was adjusted using monthly mean linear adjustment factors. The approach consisted of five processes: feature class and feature selection, asset import into Google Earth Engine, GSP model evaluation, precision assessment of GSP and observation data, and regression analysis for spatial mapping. The results reveal that when adjusted for daily data, CHIRPS and other GSP models perform much better, showing that they are suitable for precipitation estimation. This work contributes to the development of methods for estimating precipitation, particularly in areas with limited ground-based data. The advice to use ground-based observations in addition to satellite-retrieved precipitation data is intended to improve weather forecast accuracy and aid early warning systems for severe weather. Our research stresses the importance of a hybrid strategy that combines high-resolution models and satellite data for effectively managing watersheds and disaster preparedness.