Evaluation of Statistical Bias Correction Methods for GPM Precipitation: Application in Gembong Watershed, Indonesia
DOI:
10.29303/jppipa.v11i11.12650Published:
2025-11-25Downloads
Abstract
Reliable precipitation data are essential for hydrological modeling in data-scarce basins. This study evaluates five statistical bias-correction methods—Correction Factor (mean-ratio scaling), Linear Scaling (mean adjustment), Linear Regression, Local Intensity Scaling (LOCI; wet-day threshold and intensity adjustment), and Power Transformation—to improve satellite rainfall for the Gembong Watershed, Pasuruan, East Java, Indonesia. We used daily TRMM (2004–2013) and GPM IMERG (2014–2023) estimates harmonized to a common grid and time step and compared them with gauges using Pearson’s r, Nash–Sutcliffe Efficiency (NSE), and the RMSE-observation standard deviation ratio (RSR). LOCI delivered the best overall balance (NSE = 0.92; r = 0.84; RSR = 0.55), while Linear Scaling achieved a slightly lower NSE but the smallest RSR (NSE = 0.87; RSR = 0.49). Power Transformation showed limited skill (NSE = 0.57; RSR = 0.90) despite high correlation. Ranking prioritized NSE with r and RSR as supporting metrics. The coastal-lowland setting of Pasuruan—with strong convective rainfall and heterogeneous land use—makes accurate bias correction particularly consequential for flood and water-resources analysis. We conclude that LOCI’s adaptive thresholding is well-suited to such regimes and that the comparative framework aids method selection for similar data-scarce watersheds.
Keywords:
Bias correction, Coastal lowland indonesia, GPM IMERG, Hydrological modeling, Local intensity scaling (LOCI), Rainfall estimation accuracy, TRMM–GPM harmonizationReferences
Alsumaiti, T. S., Atyabi, S. A., & Ahmed, A. N. (2020). IMERG vs. CMORPH performance over arid regions. Remote Sensing, 12(9), 1426. https://doi.org/10.3390/rs12091426
Amelia, D., Wahyuni, S., & Harisuseno, D. (2021). Evaluasi kesesuaian data satelit sebagai alternatif data evaporasi di Waduk Wonorejo. Jurnal Teknik Pengairan, 12(2), 127–138. https://doi.org/10.21776/ub.pengairan.2021.012.02.05
Anjum, M. N., Ahmad, I., Khan, M. S., Nawaz, R., & Tanveer, A. (2016). Comparison of TMPA 3B42 v6 & v7 over Pakistan. Atmospheric Science Letters, 17(9), 537–545. https://doi.org/10.1002/asl.654
Andriyani, S. E., Suhartanto, E., & Sisinggih, D. (2025). Analysis of CHIRPS and GPM-IMERG and discharge modeling with transfer-function GRU in Gembong Watershed, Pasuruan. Jurnal Teknik Pengairan, 16(1), 13–25. https://doi.org/10.21776/ub.pengairan.2025.016.01.2
Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society: Series B, 26(2), 211–252. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x
Cannon, A. J. (2015). Bias correction by quantile mapping: Preserving changes in extremes? Journal of Climate, 28(17), 6938–6959. https://doi.org/10.1175/JCLI-D-14-00754.1
Cannon, A. J. (2016). Multivariate bias correction of climate model output. Journal of Climate, 29(19), 7045–7067. https://doi.org/10.1175/JCLI-D-15-0679.1
Cannon, A. J. (2018). Multivariate quantile mapping bias correction. Climate Dynamics, 50(9–10), 3217–3239. https://doi.org/10.1007/s00382-017-3580-6
Chen, J., Brissette, F. P., Chaumont, D., & Braun, M. (2013). Choosing appropriate precipitation bias-correction methods. Water Resources Research, 49(7), 4187–4205. https://doi.org/10.1002/wrcr.20331
Deswanti, B., Lestari, A. P., & Firdani, L. (2022). Influence of weather conditions on COVID-19 cases in Indonesia. Jurnal Pendidikan IPA Indonesia (JPPIPA), 8(3), 430–437. https://doi.org/10.29303/jppipa.v8i3.1851
Dwinanda, I. G., Sari, N. K., & Sasmita, I. N. (2024). Predictive mapping of hydrometeorological disaster-prone areas. Jurnal Pendidikan IPA, 10(2), 1162–1172. https://doi.org/10.29303/jppipa.v10i2.6238
Fang, G. H., Yang, J., Chen, Y. N., & Zammit, C. (2015). Comparing precipitation bias-correction methods for hydrologic impact studies. Hydrology and Earth System Sciences, 19, 2547–2569. https://doi.org/10.5194/hess-19-2547-2015
Fidari, J. S., Putri, A. S., & Suhartanto, E. (2023). Perbandingan metode FJ Mock dan NRECA di DAS Rejoso, Pasuruan. Jurnal Teknologi dan Rekayasa Sumber Daya Air, 3(2), 78–92. https://doi.org/10.21776/ub.jtresda.2023.003.002.07
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., & Engen-Skaugen, T. (2012). Downscaling RCM precipitation: Comparison of transformation methods. Hydrology and Earth System Sciences, 16, 3383–3390. https://doi.org/10.5194/hess-16-3383-2012
Gustoro, D., Sujono, J., & Karlina. (2022). Perbandingan pola distribusi hujan terukur dan satelit di DAS Progo. Jurnal Teknik Pengairan, 13(1), 23–35. https://doi.org/10.21776/ub.pengairan.2022.013.01.03
Hamed, K. H. (2008). Trend detection in hydrologic series. Journal of Hydrology, 349, 350–363. https://doi.org/10.1016/j.jhydrol.2007.11.024
Hou, A. Y., Kakar, R. K., & Neeck, S. (2014). The GPM mission. Bulletin of the American Meteorological Society, 95(5), 701–722. https://doi.org/10.1175/BAMS-D-13-00164.1
Ivanov, M. A. (2018). Intensity-dependent precipitation bias correction. Journal of Climate, 31(16), 6591–6611. https://doi.org/10.1175/JCLI-D-17-0765.1
Jauhari, M. R., Suhartanto, E., & Lufira, R. D. (2025). Alih Ragam Hujan Menjadi Debit berbasis data hujan satelit GPM dengan metode FJ Mock pada DAS Gembong, Pasuruan. Jurnal Teknologi dan Rekayasa Sumber Daya Air, 5(2), 855–866. https://doi.org/10.21776/ub.jtresda.2025.005.02.081
Jaiswal, R. (2022). Evaluation of bias-correction methods (LOCI/LS/EQM). Earth and Space Science, 9(1), e2021EA001981. https://doi.org/10.1029/2021EA001981
Kidd, C., Tan, J., & Kirschbaum, D. B. (2021). Global satellite precipitation constellation. BAMS, 102(10), E1844–E1861. https://doi.org/10.1175/BAMS-D-20-0299.1
Li, Z. (2022). IMERG and extremes: Agreement with gauges. Journal of Hydrology, 606, 127357. https://doi.org/10.1016/j.jhydrol.2021.127357
Luo, M. (2018). Bias-correction & downscaling of precipitation. Water, 10(8), 1046. https://doi.org/10.3390/w10081046
Mamenun, Pawitan, H., & Sopaheluwakan, A. (2014). Validasi & koreksi data TRMM 3B43 v7 di Indonesia. Jurnal Meteorologi dan Geofisika, 15(1), 19–28. https://doi.org/10.31172/jmg.v15i1.169
Mardyansyah, R. Y., Yulianti, N., & Rismayani, R. (2024). Communication satellite-based rainfall estimation for disaster early warning. Jurnal Pendidikan IPA, 10(12), 8607–8617. https://doi.org/10.29303/jppipa.v10i12.8409
Melanwati, R. L., Suprayogi, A., & Hapsari, N. (2023). Reconstruction of rainfall patterns with the SpVAR method. Jurnal Pendidikan IPA, 9(12), 2207–2218. https://doi.org/10.29303/jppipa.v9i12.4895
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153
Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models. Journal of Hydrology, 10(3), 282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Partarini, N. M. C., Sujono, J., & Pratiwi, E. P. A. (2021). Validation of rain thickness model based on GPM. Jurnal Teknik Pengairan, 12(2), 116–126. https://doi.org/10.21776/ub.pengairan.2021.012.02.04
Pierce, D. W., Cayan, D. R., & Maurer, E. P. (2015). Improved bias-correction techniques for hydrologic simulations. Journal of Hydrometeorology, 16(6), 2421–2442. https://doi.org/10.1175/JHM-D-14-0236.1
Ramadhan, R., Zailani, I. L., Hidayati, R., & Fahmi, A. (2022). Ground validation of GPM IMERG-F over mountainous Indonesia. Jurnal Penelitian Pendidikan IPA, 8(1), 163–170. https://doi.org/10.29303/jppipa.v8i1.1155
Ramadhan, R., Zailani, I. L., Khair, H., & Fahmi, A. (2022). Evaluation of GPM IMERG using gauges over Indonesia. Remote Sensing, 14(5), 1172. https://doi.org/10.3390/rs14051172
Safitri, R., Fadhilah, A., & Putri, R. P. (2022). Effects of land cover change and deforestation on hydro-climate indicators in Sumatra. Jurnal Penelitian Pendidikan IPA, 8(2), 307–316. https://doi.org/10.29303/jppipa.v8i2.2182
Schmidli, J., Frei, C., & Vidale, P. L. (2006). Downscaling from GCM precipitation: A benchmark introducing LOCI. International Journal of Climatology, 26(5), 679–689. https://doi.org/10.1002/joc.1287
Setiyowati, Y. A. (2025). Comparison of correlation, PBIAS, and RSR between monthly–annual IMERG and gauges in Jatigede. Jurnal Penelitian Pendidikan IPA, 11(6), 4303–4312. https://doi.org/10.29303/jppipa.v11i6.11068
Syaifullah, I., Lubis, A., & Pinem, S. (2014). Validasi TRMM 3B43 v7 di Sumatera Utara. Jurnal Meteorologi dan Geofisika, 15(2), 129–138. https://doi.org/10.31172/jmg.v15i2.180
Tan, J., Huffman, G. J., Bolvin, D. T., & Nelkin, E. J. (2019). IMERG V06: Algorithm/system changes. Journal of Atmospheric and Oceanic Technology, 36(12), 2471–2482. https://doi.org/10.1175/JTECH-D-19-0114.1
Teutschbein, C., & Seibert, J. (2012). Bias correction for hydrologic climate-change studies: Review and evaluation. Journal of Hydrology, 12–29. https://doi.org/10.1016/j.jhydrol.2012.05.052
Utami, R. T., Suhartanto, E., & Yuliani, E. (2025). Penerapan data hujan satelit GPM dan metode FJ Mock untuk alih-ragam hujan–debit di DAS Welang. Jurnal Teknologi dan Rekayasa Sumber Daya Air, 5(2), 975–984. https://doi.org/10.21776/ub.jtresda.2025.005.02.092
Velásquez, P., Messmer, M., & Raible, C. C. (2020). A bias-correction method for complex terrain. Geoscientific Model Development, 13(10), 5007–5027. https://doi.org/10.5194/gmd-13-5007-2020
Zaini, A. Z. A., Azhari, M. S., & Rahmad, A. (2023). Seasonal variation of rainfall in Indonesia under normal conditions. Jurnal Penelitian Pendidikan IPA, 9(11), 1735–1744. https://doi.org/10.29303/jppipa.v9i11.4569
Wahyuni, S., Sisinggih, D., Kriswardhana, W., & Widyaningrum, A. (2024). Rationalization of rain-gauge density using the stepwise method. Jurnal Teknik Pengairan, 15(1), 88–94. (ID—Nasional) https://doi.org/10.21776/ub.pengairan.2024.015.01.9
License
Copyright (c) 2025 M. Reza Jauhari, Ery Suhartanto
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
