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Enhancing Daily Rainfall Data Completeness Using Satellite Rainfall Estimates Cover

Enhancing Daily Rainfall Data Completeness Using Satellite Rainfall Estimates

Open Access
|Sep 2025

References

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. Pappas, C., Papalexiou, S. M., & Koutsoyiannis, D. (1955). Journal of geophysical research. Nature, 175(4449), 238. https://doi.org/10.1038/175238c0.
  26. Shin, K., Song, J. J., Bang, W., & Lee, G. (2021). Approaches with Operational Dual-Polarization Radar Data. Remote Sensing, 13(694), 1–21.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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).
  47. Motovilov, Y. G., Gottschalk, L., Engeland, K., & Rodhe, A. (1999). Validation of a distributed hydrological model against spatial observations. Agricultural and Forest Meteorology, 9899, 257–277. https://doi.org/10.1016/S0168-1923(99)00102-1.
  48. 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).
  49. 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.
  50. 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.
  51. 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.
  52. 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.
  53. 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.
  54. 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.
  55. 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).
DOI: https://doi.org/10.2478/cee-2026-0008 | Journal eISSN: 2199-6512 | Journal ISSN: 1336-5835
Language: English
Submitted on: Jun 22, 2025
Accepted on: Jul 29, 2025
Published on: Sep 5, 2025
Published by: University of Žilina
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2025 Rafika Andari, Nurhamidah Nurhamidah, Darwizal Daoed, Marzuki Marzuki, published by University of Žilina
This work is licensed under the Creative Commons Attribution 4.0 License.

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