* Corresponding Author
Analisis statistik kinerja dan koreksi kesalahan data curah hujan berbasis satelit di Provinsi Bali
https://doi.org/10.22146/mgi.99971 Putu Aryastana(1*), Cokorda Agung Yujana(2), Kadek Windy Candrayana(3), Krisna Himawan Subiyanto(4)
(1) Magister Rekayasa Infrastruktur dan Lingkungan, Fakultas Pascasarjana, Universitas Warmadewa dan Program Studi Teknik Sipil, Fakultas Teknik dan Perencanaan, Universitas Warmadewa
(2) Program Studi Teknik Sipil, Fakultas Teknik dan Perencanaan, Universitas Warmadewa dan 3Program Studi Program Profesi Insinyur, Fakultas Teknik dan Perencanaan, Universitas Warmadewa
(3) Program Studi Teknik Sipil, Fakultas Teknik dan Perencanaan, Universitas Warmadewa dan Program Studi Program Profesi Insinyur, Fakultas Teknik dan Perencanaan, Universitas Warmadewa
(4) Program Studi Teknik Geodesi, Fakultas Teknik Sipil dan Perencanaan, Institut Teknologi Nasional Malang
(*) Corresponding Author
Abstract
Abstrak.Data curah hujan yang akurat, reliabel, dan mendekati waktu nyata adalah faktor penting dalam analisis peramalan dan mitigasi bencara alam hidro klimatologi (banjir, tanah longsor, topan, dan curah hujan ekstrim), pemodelan hidrologi, prakiraan cuaca, perencanaan pertanian, manajemen ekologi, dan manajemen sumber daya air. Observasi curah hujan stasiun menghadapi kendala di Provinsi Bali, terutama pengukuran jarang ditemui di daerah terpencil dan pegunungan. Oleh karena itu, perlu mencari sumber data hujan yang dapat diandalkan seperti produk hujan berbasis satelit, yang menyediakan data dalam waktu mendekati waktu nyata (near real-time), deretan waktu hujan yang tidak terputus dengan resolusi spasial tinggi. Penelitian ini mengevaluasi kinerja produk hujan satelit global yang mendekati waktu nyata dengan 43 stasiun di Provinsi Bali. Produk curah hujan satelit yang dianalisis adalah Integrated Multi-satellitE Retrievals for Global Precipitation Measurement-Early Run (IMERG-ER) dan The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Dynamic Infrared Rain Rate near real-time (PDIR-Now). Selanjutnya, kedua data curah hujan berbasis satelit tersebut dikoreksi menggunakan tiga pendekatan, yaitu koreksi rasio bias, koreksi rata-rata deviasi, dan koreksi nilai fungsi distribusi probabilitas. Metode tradisional berbasis titik ke piksel bersama dengan pengukuran statistik kontinu, metrik kategoris, serta indeks volumetrik diimplementasikan untuk mengevaluasi kinerja produk satelit. Studi ini menunjukkan bahwa meskipun kedua dataset memiliki kelebihan masing-masing, IMERG-ER cenderung lebih konsisten dan andal dalam berbagai kondisi dibandingkan PDIR-Now, terutama setelah koreksi dilakukan. Koreksi nilai fungsi distribusi probabilitas menunjukkan peningkatan kinerja paling signifikan dibandingkan dengan metode koreksi yang lainnya. Hasil studi ini juga mempertegas bahwa koreksi kesalahan perlu dilakukan sebelum data curah hujan berbasis satelit diaplikasikan dan berbagai bidang.
Abstract. Accurate, reliable, and near-real-time rainfall data are critical factors for forecasting and mitigating hydro-meteorological natural disasters (such as floods, landslides, typhoons, and extreme rainfall), hydrological modeling, weather forecasting, agricultural planning, ecological management, and water resource management. Rainfall observations from station measurements face challenges in Bali Province, particularly due to the scarcity of measurements in remote and mountainous areas. Therefore, it is necessary to seek reliable sources of rainfall data, such as satellite-based rainfall products, which provide near real-time data, uninterrupted rainfall time series, and high spatial resolution. This research evaluates the performance of global near real-time satellite rainfall products with data from 43 stations across Bali Province. The satellite rainfall products analyzed include the Integrated Multi-satellite Retrievals for Global Precipitation Measurement-Early Run (IMERG-ER) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Dynamic Infrared Rain Rate near real-time (PDIR-Now). Subsequently, the satellite-based rainfall data were corrected using three approaches: bias ratio correction, mean deviation correction, and probability distribution function value correction. Traditional point-to-pixel methods, along with continuous statistical measurements, categorical metrics, and volumetric indices, were implemented to evaluate the performance of satellite products. The study reveals that although both datasets have their respective strengths, IMERG tends to be more consistent and reliable under various conditions compared to PERSIANN, especially after corrections are applied. The probability distribution function value correction demonstrated the most significant performance improvement compared to the other correction methods. The findings of this study also emphasize the necessity of error correction before satellite-based rainfall data is applied across various fields.
Submitted: 2024-09-14 Revisions: 2025-03-06 Accepted: 2024-09-11 Published: 2025-03-14
Keywords
References
AghaKouchak, A., Bárdossy, A., & Habib, E. (2010). Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula. Advances in Water Resources, 33(6), 624–634. https://doi.org/10.1016/j.advwatres.2010.02.010
Aghakouchak, A., & Mehran, A. (2013). Extended contingency table: Performance metrics for satellite observations and climate model simulations. Water Resources Research, 49(10), 7144–7149. https://doi.org/10.1002/wrcr.20498
Artan, G., Gadain, H., Smith, J. L., Asante, K., Bandaragoda, C. J., & Verdin, J. P. (2007). Adequacy of satellite derived rainfall data for stream flow modeling. Natural Hazards, 43(2), 167–185. https://doi.org/10.1007/s11069-007-9121-6
Aryastana, P., Dewi, L., Wahyuni, P. I., Sinarta, I. N., Punay, J. P., & Wui, J. C. H. (2024). Evaluation of Double Fusion Satellite Rainfall Dataset in Establish Rainfall Thresholds for Landslide Occurrences Over Badung Regency-Bali. In Landslide: Susceptibility, Risk Assessment and Sustainability. Advances in Natural and Technological Hazards Research, vol 52 (pp. 571–591). Springer, Cham. https://doi.org/10.1007/978-3-031-56591-5_22
Aryastana, P., Liu, C.-Y., Jong‐Dao Jou, B., Cayanan, E., Punay, J. P., & Chen, Y. (2022). Assessment of Satellite Precipitation Data Sets for High Variability and Rapid Evolution of Typhoon Precipitation Events in the Philippines. Earth and Space Science, 9(9). https://doi.org/10.1029/2022EA002382
Aryastana, P., Wahyuni, P. I., Dewi, L., Punay, J. P., Haditama, I. G. N. H. R., & Jalakam, S. P. (2023). The Quantitative Comparison of Grid Re-analysis Rainfall Products, Satellite Rainfall Products, and Hourly Rainfall Gauge Observation over Bali Province. E3S Web of Conferences, 445, 01020. https://doi.org/10.1051/e3sconf/202344501020
As-Syakur, A. R., Tanaka, T., Prasetia, R., Swardika, I. K., & Kasa, I. W. (2011). Comparison of TRMM multisatellite precipitation analysis (TMPA) products and daily-monthly gauge data over Bali. International Journal of Remote Sensing, 32(24), 8969–8982. https://doi.org/10.1080/01431161.2010.531784
Ayehu, G. T., Tadesse, T., Gessesse, B., & Dinku, T. (2018). Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia. Atmospheric Measurement Techniques, 11(4), 1921–1936. https://doi.org/10.5194/amt-11-1921-2018
Ayugi, B., Tan, G., Ullah, W., Boiyo, R., & Ongoma, V. (2019). Inter-comparison of remotely sensed precipitation datasets over Kenya during 1998–2016. Atmospheric Research, 225, 96–109. https://doi.org/10.1016/j.atmosres.2019.03.032
Brunetti, M. T., Melillo, M., Peruccacci, S., Ciabatta, L., & Brocca, L. (2018). How far are we from the use of satellite rainfall products in landslide forecasting? Remote Sensing of Environment, 210, 65–75. https://doi.org/10.1016/j.rse.2018.03.016
Caracciolo, D., Francipane, A., Viola, F., Noto, L. V., & Deidda, R. (2018). Performances of GPM satellite precipitation over the two major Mediterranean islands. Atmospheric Research, 213, 309–322. https://doi.org/10.1016/j.atmosres.2018.06.010
Chen, H., Yong, B., Kirstetter, P.-E., Wang, L., & Hong, Y. (2021). Global component analysis of errors in three satellite-only global precipitation estimates. Hydrology and Earth System Sciences, 25(6), 3087–3104. https://doi.org/10.5194/hess-25-3087-2021
Dai, Q., Han, D., Rico-Ramirez, M., & Srivastava, P. K. (2014). Multivariate distributed ensemble generator: A new scheme for ensemble radar precipitation estimation over temperate maritime climate. Journal of Hydrology, 511, 17–27. https://doi.org/10.1016/j.jhydrol.2014.01.016
Derin, Y., Anagnostou, E., Berne, A., Borga, M., Boudevillain, B., Buytaert, W., Chang, C. H., Delrieu, G., Hong, Y., Hsu, Y. C., Lavado-Casimiro, W., Manz, B., Moges, S., Nikolopoulos, E. I., Sahlu, D., Salerno, F., Rodríguez-Sánchez, J. P., Vergara, H. J., & Yilmaz, K. K. (2016). Multiregional satellite precipitation products evaluation over complex terrain. Journal of Hydrometeorology, 17(6), 1817–1836. https://doi.org/10.1175/JHM-D-15-0197.1
Dezfuli, A. K., Ichoku, C. M., Huffman, G. J., Mohr, K. I., Selker, J. S., van de Giesen, N., Hochreutener, R., & Annor, F. O. (2017). Validation of IMERG Precipitation in Africa. Journal of Hydrometeorology, 18(10), 2817–2825. https://doi.org/10.1175/JHM-D-17-0139.1
Dinku, T., Ceccato, P., Grover-Kopec, E., Lemma, M., Connor, S. J., & Ropelewski, C. F. (2007). Validation of satellite rainfall products over East Africa’s complex topography. International Journal of Remote Sensing, 28(7), 1503–1526. https://doi.org/10.1080/01431160600954688
Dinku, T., Chidzambwa, S., Ceccato, P., Connor, S. J., & Ropelewski, C. F. (2008). Validation of high-resolution satellite rainfall products over complex terrain. International Journal of Remote Sensing, 29(14), 4097–4110. https://doi.org/10.1080/01431160701772526
Dinku, T., Ruiz, F., Connor, S. J., & Ceccato, P. (2010). Validation and intercomparison of satellite rainfall estimates over Colombia. Journal of Applied Meteorology and Climatology, 49(5), 1004–1014. https://doi.org/10.1175/2009JAMC2260.1
Ebert, E. E. (2007). Methods for Verifying Satellite Precipitation Estimates. In Measuring Precipitation from Space: EURAINSAT and the Future (pp. 345–356). Springer.
Fang, J., Yang, W., Luan, Y., Du, J., Lin, A., & Zhao, L. (2019). Evaluation of the TRMM 3B42 and GPM IMERG products for extreme precipitation analysis over China. Atmospheric Research, 223(September 2018), 24–38. https://doi.org/10.1016/j.atmosres.2019.03.001
Feidas, H. (2010). Validation of satellite rainfall products over Greece. Theoretical and Applied Climatology, 99(1–2), 193–216. https://doi.org/10.1007/s00704-009-0135-8
Filho, G. R., Coelho, V. R., Freitas, E. S., Xuan, Y., Brocca, L., & Almeida, C. N. (2022). Regional-scale evaluation of 14 satellite-based precipitation products in characterising extreme events and delineating rainfall thresholds for flood hazards. Atmospheric Research, 276, 106259. https://doi.org/10.1016/j.atmosres.2022.106259
Gado, T. A., Shalaby, B. A., Guo, Y., & Rashwan, I. M. H. (2024). Assessment of Satellite-Based Precipitation Estimates over Egypt. Journal of Hydrologic Engineering, 29(1). https://doi.org/10.1061/JHYEFF.HEENG-6051
Huang, C., Hu, J., Chen, S., Zhang, A., Liang, Z., Tong, X., Xiao, L., Min, C., & Zhang, Z. (2019). How Well Can IMERG Products Capture Typhoon Extreme Precipitation Events over Southern China? Remote Sensing, 11(1), 70. https://doi.org/10.3390/rs11010070
Huang, W.-R., Chang, Y. H., & Liu, P. Y. (2018). Assessment of IMERG precipitation over Taiwan at multiple timescales. Atmospheric Research, 214(July), 239–249. https://doi.org/10.1016/j.atmosres.2018.08.004
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K.-L., Joyce, R. J., Kidd, C., Nelkin, E. J., Sorooshian, S., Stocker, E. F., Tan, J., Wolff, D. B., & Xie, P. (2020). Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG) (pp. 343–353). https://doi.org/10.1007/978-3-030-24568-9_19
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., & Tan, J. (2019). GPM IMERG Early Precipitation L3 1 day 0.1 degree x 0.1 degree V06. Goddard Earth Sciences Data and Information Services Center (GES DISC). https://doi.org/10.5067/GPM/IMERGDE/DAY/06
Hughes, D. A. (2006). Comparison of satellite rainfall data with observations from gauging station networks. Journal of Hydrology, 327(3–4), 399–410. https://doi.org/10.1016/j.jhydrol.2005.11.041
Katiraie-Boroujerdy, P. S., Nasrollahi, N., Hsu, K. lin, & Sorooshian, S. (2013). Evaluation of satellite-based precipitation estimation over Iran. Journal of Arid Environments, 97(March 2018), 205–219. https://doi.org/10.1016/j.jaridenv.2013.05.013
Kummerow, C., & Giglio, L. (1994). A Passive Microwave Technique for Estimating Rainfall and Vertical Structure Information from Space. Part II: Applications to SSM/I Data. Journal of Applied Meteorology, 33(1), 19–34. https://doi.org/10.1175/1520-0450(1994)033<0019:APMTFE>2.0.CO;2
Lee, K.-O., Uyeda, H., & Lee, D.-I. (2014). Microphysical structures associated with enhancement of convective cells over Mt. Halla, Jeju Island, Korea on 6 July 2007. Atmospheric Research, 135–136, 76–90. https://doi.org/10.1016/j.atmosres.2013.08.012
Li, Z., Yang, D., & Hong, Y. (2013). Multi-scale evaluation of high-resolution multi-sensor blended global precipitation products over the Yangtze River. Journal of Hydrology, 500, 157–169. https://doi.org/10.1016/j.jhydrol.2013.07.023
Liao, Z., Hong, Y., Wang, J., Fukuoka, H., Sassa, K., Karnawati, D., & Fathani, F. (2010). Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets. Landslides, 7(3), 317–324. https://doi.org/10.1007/s10346-010-0219-7
Liu, C.-Y., Aryastana, P., Liu, G.-R., & Huang, W.-R. (2020). Assessment of satellite precipitation product estimates over Bali Island. Atmospheric Research, 244, 105032. https://doi.org/10.1016/j.atmosres.2020.105032
Lu, X., Tang, G., Wei, M., Yang, L., & Zhang, T. (2018). Evaluation of multi-satellite precipitation products in Xinjiang, China. Internation Journal of Remote Sensing, 39(21), 7437–7462.
Ma, Y., Yang, Y., Han, Z., Tang, G., Maguire, L., Chu, Z., & Hong, Y. (2018). Comprehensive evaluation of Ensemble Multi-Satellite Precipitation Dataset using the Dynamic Bayesian Model Averaging scheme over the Tibetan plateau. Journal of Hydrology, 556, 634–644. https://doi.org/10.1016/j.jhydrol.2017.11.050
Marra, F., Morin, E., Peleg, N., Mei, Y., & Anagnostou, E. N. (2017). Intensity–duration–frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean. Hydrology and Earth System Sciences, 21(5), 2389–2404. https://doi.org/10.5194/hess-21-2389-2017
Morbidelli, R., Saltalippi, C., Dari, J., & Flammini, A. (2021). A Review on Rainfall Data Resolution and Its Role in the Hydrological Practice. Water, 13(8), 1012. https://doi.org/10.3390/w13081012
Muntohar, A. S., Mavrouli, O., Jetten, V. G., van Westen, C. J., & Hidayat, R. (2021). Development of Landslide Early Warning System Based on the Satellite-Derived Rainfall Threshold in Indonesia. In N. Casagli, V. Tofani, K. Sassa, P. T. Bobrowsky, & K. Takara (Eds.), Understanding and Reducing Landslide Disaster Risk (Issue January, pp. 227–235). Springer, Cham. https://doi.org/10.1007/978-3-030-60311-3_26
New, M., Todd, M., Hulme, M., & Jones, P. (2001). Precipitation measurements and trends in the twentieth century. International Journal of Climatology, 21(15), 1889–1922. https://doi.org/10.1002/joc.680
Nguyen, P., Ombadi, M., Sorooshian, S., Hsu, K., AghaKouchak, A., Braithwaite, D., Ashouri, H., & Rose Thorstensen, A. (2018). The PERSIANN family of global satellite precipitation data: A review and evaluation of products. Hydrology and Earth System Sciences, 22(11), 5801–5816. https://doi.org/10.5194/hess-22-5801-2018
Nguyen, P., Shearer, E. J., Ombadi, M., Gorooh, V. A., Hsu, K., Sorooshian, S., Logan, W. S., & Ralph, M. (2020). PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation. Bulletin of the American Meteorological Society, 101(3), E286–E302. https://doi.org/10.1175/BAMS-D-19-0118.1
Nikolopoulos, E. I., Destro, E., Maggioni, V., Marra, F., & Borga, M. (2017). Satellite rainfall estimates for debris flow prediction: An evaluation based on rainfall accumulation-duration thresholds. Journal of Hydrometeorology, 18(8), 2207–2214. https://doi.org/10.1175/JHM-D-17-0052.1
Nurfaijin, N. (2013). Analisis Karakteristik Hujan untuk Pendugaan Debit Aliran Rencana Sungai Anafri di Kota Jayapura. Majalah Geografi Indonesia, 27(1), 78–103. https://doi.org/http://dx.doi.org/10.22146/mgi.13451
Putra, M., Rosid, M. S., & Handoko, D. (2024). A Review of Rainfall Estimation in Indonesia : Data Sources ,. 542–561.
Rahmawati, N., & Lubczynski, M. W. (2018). Validation of satellite daily rainfall estimates in the complex terrain of Bali Island, Indonesia. Theoretical and Applied Climatology, 134(1–2), 513–532. https://doi.org/10.1007/s00704-017-2290-7
Rossi, M., Luciani, S., Valigi, D., Kirschbaum, D., Brunetti, M. T., Peruccacci, S., & Guzzetti, F. (2017). Statistical approaches for the definition of landslide rainfall thresholds and their uncertainty using rain gauge and satellite data. Geomorphology, 285, 16–27. https://doi.org/10.1016/j.geomorph.2017.02.001
Salio, P., Hobouchian, M. P., García Skabar, Y., & Vila, D. (2015). Evaluation of high-resolution satellite precipitation estimates over southern South America using a dense rain gauge network. Atmospheric Research, 163, 146–161. https://doi.org/10.1016/j.atmosres.2014.11.017
Shi, J., Yuan, F., Shi, C., Zhao, C., Zhang, L., Ren, L., Zhu, Y., Jiang, S., & Liu, Y. (2020). Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region. Water, 12(4), 1006. https://doi.org/10.3390/w12041006
Sohn, B. J., Ryu, G. H., Song, H. J., & Ou, M. L. (2013). Characteristic features of warm-type rain producing heavy rainfall over the korean peninsula inferred from TRMM measurements. Monthly Weather Review, 141(11), 3873–3888. https://doi.org/10.1175/MWR-D-13-00075.1
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., & Hsu, K. (2018). A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons. Reviews of Geophysics, 56(1), 79–107. https://doi.org/10.1002/2017RG000574
Sun, R., Yuan, H., Liu, X., & Jiang, X. (2016). Evaluation of the latest satellite–gauge precipitation products and their hydrologic applications over the Huaihe River basin. Journal of Hydrology, 536, 302–319. https://doi.org/10.1016/j.jhydrol.2016.02.054
Sunilkumar, K., Narayana Rao, T., Saikranthi, K., & Purnachandra Rao, M. (2015). Comprehensive evaluation of multisatellite precipitation estimates over India using gridded rainfall data. Journal of Geophysical Research: Atmospheres, 120(17), 8987–9005. https://doi.org/10.1002/2015JD023437
Tambie, J., Campus, M., & Ramlal, B. (2024). Comparison of GPM IMERG and PERSIANN-CDR satellite-derived precipitation estimate products over Trinidad Comparison of GPM IMERG and PERSIANN-CDR satellite- derived precipitation estimate products over Trinidad. February.
Tan, M. L., & Duan, Z. (2017). Assessment of GPM and TRMM precipitation products over Singapore. Remote Sensing, 9(7). https://doi.org/10.3390/rs9070720
Tan, M. L., Ibrahim, A. L., Duan, Z., Cracknell, A. P., & Chaplot, V. (2015). Evaluation of six high-resolution satellite and ground-based precipitation products over Malaysia. Remote Sensing, 7(2), 1504–1528. https://doi.org/10.3390/rs70201504
Tan, M. L., & Santo, H. (2018). Comparison of GPM IMERG, TMPA 3B42 and PERSIANN-CDR satellite precipitation products over Malaysia. Atmospheric Research, 202(November 2017), 63–76. https://doi.org/10.1016/j.atmosres.2017.11.006
Tang, G., Ma, Y., Long, D., Zhong, L., & Hong, Y. (2016). Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales. Journal of Hydrology, 533, 152–167. https://doi.org/10.1016/j.jhydrol.2015.12.008
Tang, X., Li, H., Qin, G., Huang, Y., & Qi, Y. (2023). Evaluation of Satellite-Based Precipitation Products over Complex Topography in Mountainous Southwestern China. Remote Sensing, 15(2), 473. https://doi.org/10.3390/rs15020473
Tao, W.-K., Wu, D., Lang, S., Chern, J.-D., Peters‐Lidard, C., Fridlind, A., & Matsui, T. (2016). High‐resolution NU‐WRF simulations of a deep convective‐precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations. Journal of Geophysical Research: Atmospheres, 121(3), 1278–1305. https://doi.org/10.1002/2015JD023986
Torre, E. M. de la, Trinidad, J. G., Ramírez, E. G., Capetillo, C. F. B., Ferreira, H. E. J., Almaraz, H. B., & Recendez, M. I. R. (2024). Estimation of Rainfall via IMERG-FR and Its Relationship with the Records of a Rain Gauge Network with Spatio-Temporal Variation, Case of Study: Mexican Semi-Arid Region. Remote Sensing, 16(2), 273. https://doi.org/10.3390/rs16020273
Wang, H., Yuan, Y., Zeng, S., Li, W., & Tang, X. (2021). Evaluation of satellite-based precipitation products from GPM IMERG and GSMaP over the three-river headwaters region, China. Hydrology Research, 52(6), 1328–1343. https://doi.org/10.2166/NH.2021.029
Wong, J. S., Razavi, S., Bonsal, B. R., Wheater, H. S., & Asong, Z. E. (2017). Inter-comparison of daily precipitation products for large-scale hydro-climatic applications over Canada. Hydrology and Earth System Sciences, 21(4), 2163–2185. https://doi.org/10.5194/hess-21-2163-2017
Xu, R., Tian, F., Yang, L., Hu, H., Lu, H., & Hou, A. (2017). Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan plateau based on a high-density rain gauge network. Journal of Geophysical Research, 122(2), 910–924. https://doi.org/10.1002/2016JD025418
Yamamoto, M. K., & Shige, S. (2015). Implementation of an orographic/nonorographic rainfall classification scheme in the GSMaP algorithm for microwave radiometers. Atmospheric Research, 163, 36–47. https://doi.org/10.1016/j.atmosres.2014.07.024
Yuda, I. W. A., Prasetia, R., As-Syakur, A. R., Osawa, T., & Nagai, M. (2020). An assessment of IMERG rainfall products over Bali at multiple time scale. E3S Web of Conferences, 153, 1–12. https://doi.org/10.1051/e3sconf/202015302001
Zhu, Y., Lin, Z., Wang, J., Zhao, Y., & He, F. (2016). Impacts of Climate Changes on Water Resources in Yellow River Basin, China. Procedia Engineering, 154, 687–695. https://doi.org/10.1016/j.proeng.2016.07.570
DOI: https://doi.org/10.22146/mgi.99971
Article Metrics
Abstract views : 1635
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Putu Aryastana, Cokorda Agung Yujana, Kadek Windy Candrayana, Krisna Himawan Subiyanto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Volume 35 No 2 the Year 2021 for Volume 39 No 1 the Year 2025
ISSN 0215-1790 (print) ISSN 2540-945X (online)