Gatot, Rudiantoro (2025) Pengaruh Musim Terhadap Bias Koreksi Data Estimasi Curah Hujan (CHIRPS) Di Pulau Sumatera. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
CHIRPS merupakan produk estimasi curah hujan yang banyak dimanfaatkan untuk layanan iklim, namun akurasinya bervariasi antarmusim dan antar-zona geografi di Sumatera. Penelitian ini mengkaji pengaruh musim terhadap kinerja koreksi bias CHIRPS sekaligus membandingkan beberapa metode. Data yang digunakan berupa CHIRPS bulanan periode 2005–2024 dan data penakar hujan BMKG yang tervalidasi. Analisis distratifikasi menurut empat musim (DJF, MAM, JJA, SON) dan tiga zona utama (pesisir barat, pegunungan, dataran timur). Metode yang dibandingkan meliputi BRANDES, Quantile Mapping (QM), Kriging with External Drift (KED), pendekatan Bayes (BAYES), Rank-Based Distribution Correction (RDBC), Random Forest (RF), model linier (LM), Generalized Additive Model (GAM), dan Statistical Objective Analysis (SOA). Kinerja dinilai menggunakan metrik kontinu (RMSE, MAE), korelasi Pearson (Cor), serta metrik kategorikal (POD, FAR, CSI, ACC) dan BIAS.Hasil menunjukkan BRANDES memberikan skor keseluruhan tertinggi karena peningkatan yang seimbang pada berbagai metrik, QM paling konsisten menurunkan RMSE/MAE, sedangkan RF menghasilkan COR tertinggi tetapi besaran kesalahannya masih relatif besar. Pengaruh musim dan morfologi wilayah menonjol: underestimate paling kuat terjadi di zona pegunungan pada musim hujan, sementara bias bercampur muncul di dataran timur. Temuan ini menegaskan bahwa pemilihan metode koreksi bias perlu disesuaikan dengan musim dan zona serta mendukung pemanfaatan CHIRPS yang telah dikoreksi untuk layanan iklim dan pengambilan keputusan di Sumatera
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CHIRPS is a rainfall estimation product widely used for climate services, but its accuracy varies between seasons and physiographic zones in Sumatra. This study examines the influence of seasonality on the performance of CHIRPS bias correction and compares several methods. The data used are monthly CHIRPS data for the 2005–2024 period and validated BMKG rain gauge data. The analysis is stratified by four seasons (DJF, MAM, JJA, SON) and three main zones (west coast, mountains, eastern plains). The methods compared include BRANDES, Quantile Mapping (QM), Kriging with External Drift (KED), the Bayesian approach (BAYES), Rank-Based Distribution Correction (RDBC), Random Forest (RF), linear models (LM), Generalized Additive Models (GAM), and Statistical Objective Analysis (SOA). Performance is assessed using continuous metrics (RMSE, MAE), Pearson correlation (COR), categorical metrics (POD, FAR, CSI, ACC), and BIAS. The results show that BRANDES provides the highest overall score due to balanced improvements across metrics. QM most consistently reduces RMSE/MAE, while RF produces the highest COR but its error magnitude is still relatively large. The influence of season and regional morphology is prominent: the strongest underestimation occurs in the mountainous zone during the rainy season, while mixed biases appear in the eastern plains. These findings emphasize the need for bias correction methods to be tailored to the season and zone and support the use of corrected CHIRPS for climate services and decision-making in Sumatra
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | - |
| Uncontrolled Keywords: | CHIRPS, rainfall estimation,satelit data,koreksi,validasiCHIRPS, rainfall estimation, satellite data,correction,validation |
| Subjects: | G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography > GA102.4.R44 Cartography--Remote sensing |
| Divisions: | Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29202-(S1) Undergraduate Thesis |
| Depositing User: | Mr Gatot Rudiantoro |
| Date Deposited: | 27 Jan 2026 01:33 |
| Last Modified: | 27 Jan 2026 01:33 |
| URI: | http://repository.its.ac.id/id/eprint/130365 |
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