Enhancing Daily Rainfall Data Completeness Using Satellite Rainfall Estimates

Andari, Rafika; Nurhamidah, Nurhamidah; Daoed, Darwizal; Marzuki, Marzuki

doi:10.2478/cee-2026-0008

Abstract

The availability of complete daily rainfall data is crucial for various hydrological, climatological, and meteorological studies. However, observed data often suffer from gaps due to equipment failures or station relocations. This study aims to improve the completeness of daily rainfall records by utilizing satellite rainfall estimates from TRMM, GPM-IMERG, and GSMaP products. To estimate the missing data, both linear regression and regional weighting methods were employed. A specific case study was conducted in the Kuranji watershed, Padang, Indonesia, using data spanning 2014 to 2016. The results indicate that the linear regression method based on satellite data, particularly GPM-IMERG, exhibits a higher correlation with observed data compared to the regional weighting method at most locations and across the study years. The results show that the regional weighting method was superior in 2014, while linear regression with GPM-IMERG and GSMaP was better in 2015 and 2016 in terms of accuracy, although regional weighting showed a lower relative bias. Nevertheless, the regional weighting method demonstrated a lower relative bias. This study highlights the potential of satellite-based rainfall data as a viable alternative for filling missing rainfall records. However, further calibration and validation are necessary to address spatial variability and inaccuracies during high-intensity rainfall events.

References

Qutbudin, I., Shiru, M. S., Sharafati, A., Ahmed, K., Al-Ansari, N., Yaseen, Z. M., Shahid, S., & Wang, X. (2019). Seasonal drought pattern changes due to climate variability: Case study in Afghanistan. Water (Switzerland), 11(5). https://doi.org/10.3390/w11051096.
Search in Google Scholar Back to article
Halecki, W., Młyńska, A., Sionkowski, T., & Chmielowski, K. (2023). Linking elevated rainfall with sewage discharge volume. Ochrona Srodowiska i Zasobow Naturalnych, 34(4), 135–146. https://doi.org/10.2478/oszn-2023-0020.
Search in Google Scholar Back to article
Abd Elrahman, S. I. M., & Ataalmanan, I. M. I. (2023). Determination of the Hydrological and Morphometric Characteristics Using GIS. Civil and Environmental Engineering, 19(1), 39–47. https://doi.org/10.2478/cee-2023-0004.
Search in Google Scholar Back to article
Smrčková, D., Chromčák, J., Mužík, J., Ižvoltová, J., & Kostelecký, J. (2024). Space Geodesy Data Implementation for Earth System Geodynamics Monitoring: a Case Study of the Aegean Microplate. Civil and Environmental Engineering, 20(2), 1203–1220. https://doi.org/10.2478/cee-2024-0088.
Search in Google Scholar Back to article
Liu, J., & Niyogi, D. (2019). Meta-analysis of urbanization impact on rainfall modification. Scientific Reports, 9(1), 1–14. https://doi.org/10.1038/s41598-019-42494-2.
Search in Google Scholar Back to article
Jeřábek, J., & Kavka, P. (2024). Sensitivity and uncertainty analysis of a surface runoff model using ensemble of artificial rainfall experiments. Journal of Hydrology and Hydromechanics, 72(4), 466–485. https://doi.org/10.2478/johh-2024-0021.
Search in Google Scholar Back to article
Armanuos, A. M., Al-Ansari, N., & Yaseen, Z. M. (2020). Cross assessment of twenty-one different methods for missing precipitation data estimation. Atmosphere, 11(4), 1–35. https://doi.org/10.3390/ATMOS11040389.
Search in Google Scholar Back to article
Wang, X., Shi, S., Zhu, L., Nie, Y., & Lai, G. (2023). Traditional and Novel Methods of Rainfall Observation and Measurement: A Review. Journal of Hydrometeorology, 24(12), 2153–2176. https://doi.org/10.1175/JHM-D-22-0122.1.
Search in Google Scholar Back to article
Duarte, L. V., Formiga, K. T. M., & Costa, V. A. F. (2022). Comparison of Methods for Filling Daily and Monthly Rainfall Missing Data: Statistical Models or Imputation of Satellite Retrievals? Water (Switzerland), 14(19). https://doi.org/10.3390/w14193144.
Search in Google Scholar Back to article
Hamzah, F. B., Hamzah, F. M., Razali, S. F. M., & Samad, H. (2021). A comparison of multiple imputation methods for recovering missing data in hydrological studies. Civil Engineering Journal (Iran), 7(9), 1608–1619. https://doi.org/10.28991/cej-2021-03091747.
Search in Google Scholar Back to article
Caldera, H. P. G. M., Piyathisse, V. R. P. C., & Nandalal, K. D. W. (2016). A Comparison of Methods of Estimating Missing Daily Rainfall Data. Engineer: Journal of the Institution of Engineers, Sri Lanka, 49(4), 1. https://doi.org/10.4038/engineer.v49i4.7232.
Search in Google Scholar Back to article
Adilah, N. A. A. G., & Hannani, H. (2021). Comparison of Methods to Estimate Missing Rainfall Data for Short Term Period at UMP Gambang. IOP Conference Series: Earth and Environmental Science, 682(1). https://doi.org/10.1088/1755-1315/682/1/012027.
Search in Google Scholar Back to article
Sattari, M. T., Rezazadeh-Joudi, A., & Kusiak, A. (2017). Assessment of different methods for estimation of missing data in precipitation studies. Hydrology Research, 48(4), 1032–1044. https://doi.org/10.2166/nh.2016.364.
Search in Google Scholar Back to article
Wangwongchai, A., Waqas, M., Dechpichai, P., Hlaing, P. T., Ahmad, S., & Humphries, U. W. (2023). Imputation of missing daily rainfall data; A comparison between artificial intelligence and statistical techniques. MethodsX, 11(October), 102459. https://doi.org/10.1016/j.mex.2023.102459.
Search in Google Scholar Back to article
Teegavarapu, R. S. V., & Chandramouli, V. (2005). Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. Journal of Hydrology, 312(1–4), 191–206. https://doi.org/10.1016/j.jhydrol.2005.02.015.
Search in Google Scholar Back to article
Strachan, S., Kelsey, E. P., Brown, R. F., Dascalu, S., Harris, F., Kent, G., Lyles, B., McCurdy, G., Slater, D., & Smith, K. (2016). Filling the Data Gaps in Mountain Climate Observatories Through Advanced Technology, Refined Instrument Siting, and a Focus on Gradients. Mountain Research and Development, 36(4), 518–527. https://doi.org/10.1659/MRD-JOURNAL-D-16-00028.1.
Search in Google Scholar Back to article
Borga, M., & Vizzaccaro, A. (1997). On the interpolation of hydrologic variables: Formal equivalence of multiquadratic surface fitting and kriging. Journal of Hydrology, 195(1–4), 160–171. https://doi.org/10.1016/S0022-1694(96)03250-7.
Search in Google Scholar Back to article
Plouffe, C. C. F., Robertson, C., & Chandrapala, L. (2015). Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka. Environmental Modelling and Software, 67, 57–71. https://doi.org/10.1016/j.envsoft.2015.01.011.
Search in Google Scholar Back to article
Xu, P., Wang, D., Singh, V. P., Wang, Y., Wu, J., Wang, L., Zou, X., Liu, J., Zou, Y., & He, R. (2018). A kriging and entropy-based approach to raingauge network design. Environmental Research, 161(August 2017), 61–75. https://doi.org/10.1016/j.envres.2017.10.038.
Search in Google Scholar Back to article
Rohma, N. N. (2022). Estimation of Ordinary Kriging Method with Jackknife Technique on Rainfall Data in Malang Raya. International Journal on Information and Communication Technology (IJoICT), 8(2), 22–39. https://doi.org/10.21108/ijoict.v8i2.678.
Search in Google Scholar Back to article
Beran, J., Liu, H., & Ghosh, S. (2020). On aggregation of strongly dependent time series. Scandinavian Journal of Statistics, 47(3), 690–710. https://doi.org/10.1111/sjos.12421.
Search in Google Scholar Back to article
Hasan, M. M., & Croke, B. F. W. (2013). Filling gaps in daily rainfall data: A statistical approach. Proceedings - 20th International Congress on Modelling and Simulation, MODSIM 2013, December, 380–386. https://doi.org/10.36334/modsim.2013.a9.hasan.
Search in Google Scholar Back to article
Hristopulos, D. T., & Baxevani, A. (2020). Effective probability distribution approximation for the reconstruction of missing data. Stochastic Environmental Research and Risk Assessment, 34(2), 235–249. https://doi.org/10.1007/s00477-020-01765-5.
Search in Google Scholar Back to article
Nathans, L. L., Oswald, F. L., & Nimon, K. (2012). Interpreting multiple linear regression: A guidebook of variable importance. Practical Assessment, Research and Evaluation, 17(9), 1–19.
Search in Google Scholar Back to article
Pappas, C., Papalexiou, S. M., & Koutsoyiannis, D. (1955). Journal of geophysical research. Nature, 175(4449), 238. https://doi.org/10.1038/175238c0.
Search in Google Scholar Back to article
Shin, K., Song, J. J., Bang, W., & Lee, G. (2021). Approaches with Operational Dual-Polarization Radar Data. Remote Sensing, 13(694), 1–21.
Search in Google Scholar Back to article
Longman, R. J., Newman, A. J., Giambelluca, T. W., & Lucas, M. (2020). Characterizing the uncertainty and assessing the value of gap-filled daily rainfall data in hawaii. Journal of Applied Meteorology and Climatology, 59(7), 1261–1276. https://doi.org/10.1175/JAMC-D-20-0007.1.
Search in Google Scholar Back to article
Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., & Levizzani, V. (2014). Journal of Geophysical Research : Atmospheres rainfall from satellite soil moisture data. Journal of Geophysical Research: Atmospheres, 119, 5128–5141. https://doi.org/10.1002/2014JD021489.
Search in Google Scholar Back to article
Haloho, L. S., & Supriyadi, A. A. (2024). Utilization of satellite technology in communication systems, disaster monitoring, border surveillance, and military intelligence: a literature review. Remote Sensing Technology in Defense and Environment, 1(1), 36–44. https://doi.org/10.61511/rstde.v1i1.2024.842.
Search in Google Scholar Back to article
Siabi, N., Sanaeinejad, S. H., & Ghahraman, B. (2020). Comprehensive evaluation of a spatio-temporal gap filling algorithm: Using remotely sensed precipitation, LST and ET data. Journal of Environmental Management, 261(March), 110228. https://doi.org/10.1016/j.jenvman.2020.110228.
Search in Google Scholar Back to article
de Moraes Cordeiro, A. L., & Blanco, C. J. C. (2021). Assessment of satellite products for filling rainfall data gaps in the Amazon region. Natural Resource Modeling, 34(2), 1–21. https://doi.org/10.1111/nrm.12298.
Search in Google Scholar Back to article
Abu Romman, Z., Al-Bakri, J., & Al Kuisi, M. (2021). Comparison of methods for filling in gaps in monthly rainfall series in arid regions. International Journal of Climatology, 41(15), 6674–6689. https://doi.org/10.1002/joc.7219.
Search in Google Scholar Back to article
Andari, R., Nurhamidah, N., Daoed, D., & Marzuki. (2024a). Evaluation of bias correction methods for multi-satellite rainfall estimation products. IOP Conference Series: Earth and Environmental Science, 1317(1). https://doi.org/10.1088/1755-1315/1317/1/012008.
Search in Google Scholar Back to article
Rozante, J. R., Vila, D. A., Chiquetto, J. B., Fernandes, A. de A., & Alvim, D. S. (2018). Evaluation of TRMM/GPM blended daily products over Brazil. Remote Sensing, 10(6), 1–17. https://doi.org/10.3390/rs10060882.
Search in Google Scholar Back to article
Elnashar, A., Zeng, H., Wu, B., Zhang, N., Tian, F., Zhang, M., Zhu, W., Yan, N., Chen, Z., Sun, Z., Wu, X., & Li, Y. (2020). Downscaling TRMM monthly precipitation using google earth engine and google cloud computing. Remote Sensing, 12(23), 1–22. https://doi.org/10.3390/rs12233860.
Search in Google Scholar Back to article
Ramadhan, R., Yusnaini, H., Marzuki, M., Muharsyah, R., Suryanto, W., Sholihun, S., Vonnisa, M., Harmadi, H., Ningsih, A. P., Battaglia, A., Hashiguchi, H., & Tokay, A. (2022). Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales. Remote Sensing, 14(5), 1–24. https://doi.org/10.3390/rs14051172.
Search in Google Scholar Back to article
Zhou, Z., Guo, B., Xing, W., Zhou, J., Xu, F., & Xu, Y. (2020). Comprehensive evaluation of latest GPM era IMERG and GSMaP precipitation products over mainland China. Atmospheric Research, 246. https://doi.org/10.1016/j.atmosres.2020.105132.
Search in Google Scholar Back to article
Ramadhan, R., Marzuki, M., Yusnaini, H., Muharsyah, R., Suryanto, W., Sholihun, S., Vonnisa, M., Battaglia, A., & Hashiguchi, H. (2022). Capability of GPM IMERG Products for Extreme Precipitation Analysis over the Indonesian Maritime Continent. Remote Sensing, 14(2). https://doi.org/10.3390/rs14020412.
Search in Google Scholar Back to article
Ramadhan, R., Marzuki, M., Yusnaini, H., Muharsyah, R., Tangang, F., Vonnisa, M., & Harmadi, H. (2023). A Preliminary Assessment of the GSMaP Version 08 Products over Indonesian Maritime Continent against Gauge Data. Remote Sensing, 15(4), 1115. https://doi.org/10.3390/rs15041115.
Search in Google Scholar Back to article
Nepal, B., Shrestha, D., Sharma, S., Shrestha, M. S., Aryal, D., & Shrestha, N. (2021). Assessment of GPM-Era satellite products’ (IMERG and GSMaP) ability to detect precipitation extremes over mountainous country nepal. Atmosphere, 12(2), 254. https://doi.org/10.3390/atmos12020254.
Search in Google Scholar Back to article
dos Santos, E. P., Dias, R. L. S., Maciel, I. P., Kolling Neto, A., & da Silva, D. D. (2021). Estimation of missing hydrological data in monthly rainfall series using meteorological satellite data. Environmental Earth Sciences, 80(3), 1–9. https://doi.org/10.1007/s12665-021-09409-9.
Search in Google Scholar Back to article
Elshorbagy, A. A., Panu, U. S., & Simonovic, S. P. (2000). Group-based estimation of missing hydrological data: I. Approach and general methodology. Hydrological Sciences Journal, 45(6), 849–866. https://doi.org/10.1080/02626660009492388.
Search in Google Scholar Back to article
Yozgatligil, C., Aslan, S., Iyigun, C., & Batmaz, I. (2013). Comparison of missing value imputation methods in time series: The case of Turkish meteorological data. Theoretical and Applied Climatology, 112(1–2), 143–167. https://doi.org/10.1007/s00704-012-0723-x.
Search in Google Scholar Back to article
Gomes, E. P., Blanco, C. J. C., & Pessoa, F. C. L. (2018). Regionalization of precipitation with determination of homogeneous regions via fuzzy c-means. Rbrh, 23(0). https://doi.org/10.1590/2318-0331.231820180079.
Search in Google Scholar Back to article
Byun, J., Kim, H. J., Kang, N., Yoon, J., Hwang, S., & Jun, C. (2024). Optimizing Temporal Weighting Functions to Improve Rainfall Prediction Accuracy in Merged Numerical Weather Prediction Models for the Korean Peninsula. Remote Sensing, 16(16). https://doi.org/10.3390/rs16162904.
Search in Google Scholar Back to article
McCuen, R. H., Knight, Z., & Cutter, A. G. (2006). Evaluation of the Nash–Sutcliffe Efficiency Index. Journal of Hydrologic Engineering, 11(6), 597–602. https://doi.org/10.1061/(asce)1084-0699(2006)11:6(597).
Search in Google Scholar Back to article
Motovilov, Y. G., Gottschalk, L., Engeland, K., & Rodhe, A. (1999). Validation of a distributed hydrological model against spatial observations. Agricultural and Forest Meteorology, 98–99, 257–277. https://doi.org/10.1016/S0168-1923(99)00102-1.
Search in Google Scholar Back to article
Van Liew, M. W., Veith, T. L., Bosch, D. D., & Arnold, J. G. (2007). Suitability of SWAT for the Conservation Effects Assessment Project: Comparison on USDA Agricultural Research Service Watersheds. Journal of Hydrologic Engineering, 12(2), 173–189. https://doi.org/10.1061/(asce)1084-0699(2007)12:2(173).
Search in Google Scholar Back to article
Soares, A., Paz, A., & Piccilli, D. (2016). Avaliação das estimativas de chuva do satélite TRMM no Estado da Paraíba / Assessment of rainfall estimates of TRMM satellite on Paraíba state. Revista Brasileira de Recursos Hídricos, 21(2), 288–299. https://doi.org/10.21168/rbrh.v21n2.p288-299.
Search in Google Scholar Back to article
Andari, R., Nurhamidah, N., Daoed, D., & Marzuki, M. (2024b). Validation of TRMM and GPM Satellite Data Using Daily Precipitation Observations. International Journal on Advanced Science, Engineering and Information Technology, 14(2), 555–562. https://doi.org/10.18517/ijaseit.14.2.18980.
Search in Google Scholar Back to article
Portuguez-maurtua, M., Arumi, J. L., Lagos, O., Stehr, A., & Arquiñigo, N. M. (2022). Filling Gaps in Daily Precipitation Series Using Regression and Machine Learning in Inter-Andean Watersheds. Water (Switzerland), 14(11). https://doi.org/10.3390/w14111799.
Search in Google Scholar Back to article
Camuffo, D., Becherini, F., della Valle, A., & Zanini, V. (2022). A comparison between different methods to fill gaps in early precipitation series. Environmental Earth Sciences, 81(13), 1–14. https://doi.org/10.1007/s12665-022-10467-w.
Search in Google Scholar Back to article
Bárdossy, A., & Pegram, G. (2014). Infilling missing precipitation records - A comparison of a new copula-based method with other techniques. Journal of Hydrology, 519(PA), 1162–1170. https://doi.org/10.1016/j.jhydrol.2014.08.025.
Search in Google Scholar Back to article
Chua, Z. W., Kuleshov, Y., Watkins, A. B., Choy, S., & Sun, C. (2023). A Statistical Interpolation of Satellite Data with Rain Gauge Data over Papua New Guinea. Journal of Hydrometeorology, 24(12), 2369–2387. https://doi.org/10.1175/JHMD-23-0035.1.
Search in Google Scholar Back to article
Lutfiah, Q., Purnaditya, N. P., Priyambodho, B. A., & Wigati, R. (2024). Evaluation Of Pdir-Now Satellite Rainfall Data On Observational Rainfall Data ( Case Study : Taktakan District , Serang City , Banten ). 13(2).
Search in Google Scholar Back to article

Enhancing Daily Rainfall Data Completeness Using Satellite Rainfall Estimates

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