Satellite images for rainfall estimation in Espírito Santo State, Brazil

Lucas Silveira Fuentes; José Antonio Tosta dos Reis; Antônio Sérgio Ferreira Mendonça; Fernando das Graças Braga da Silva; Matheus David Guimarães Barbedo

doi:10.5327/Z2176-94782228

Authors

Lucas Silveira Fuentes Universidade Federal do Espírito Santo (UFES) - Brazil https://orcid.org/0009-0003-4649-4675
José Antonio Tosta dos Reis Universidade Federal do Espírito Santo (UFES) - Brazil https://orcid.org/0000-0001-9916-1469
Antônio Sérgio Ferreira Mendonça Universidade Federal do Espírito Santo (UFES) - Brasil https://orcid.org/0000-0003-4273-0266
Fernando das Graças Braga da Silva Universidade Federal de Itajubá (UNIFEI) - Brasil https://orcid.org/0000-0002-3803-2257
Matheus David Guimarães Barbedo Universidade Federal de Itajubá (UNIFEI) - Brazil https://orcid.org/0000-0001-9261-3696

DOI:

https://doi.org/10.5327/Z2176-94782228

Keywords:

tropical rainfall measuring mission; global precipitation measurement; rain gauges; remote sensing.

Abstract

Due to the inadequate distribution, scarcity, or operational problems of rain gauges or pluviographs, remote sensing has been considered a relevant alternative for characterizing the regime and measuring the rainfall intensities. The present paper evaluated estimates of annual precipitation totals in the state of Espírito Santo, Brazil, established from conventional monitoring conducted by rain gauges and the manipulation of satellite images. Additionally, it assessed the quality of estimates during El Niño and La Niña years. For the study, the 3B42 version seven products from the Tropical Rainfall Measuring Mission (TRMM) satellite and the 3IMERGDF version six products from the Global Precipitation Measurement (GPM) satellite were used. These satellites were developed through a partnership between the National Aeronautics and Space Administration and the Japan Aerospace Exploration Agency. Historical series from the years 2001–2019 of 83 rain gauge stations installed and operating in the state of Espírito Santo and its surroundings were analyzed, along with satellite images from TRMM and GPM for spatial grids of 25 and 10 km, respectively. The parameters recommended by the International Precipitation Working Group were used to conduct the analysis. The TRMM satellite performed better during La Niña periods (R²=0.941; root mean square error [RMSE]=44.21 mm; ME=14.87 mm), surpassing the results observed in El Niño (R²=0.885; RMSE=141.5 mm; ME=−87.14 mm). Although the GPM satellite performed stably between El Niño and La Niña periods, with systematic underestimation (bias≈0.93) and similar ME (ME≈79 mm), it showed a lower correlation with rain gauge records (R²≈0.77) than the TRMM satellite. The results indicated that the use of satellite images was a consistent alternative for the appropriation of annual precipitation totals and that the TRMM satellite presented more accurate estimates, both for long-term analysis and for the El Niño and La Niña periods.

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References

Adler, R.F.; Huffman, G.J.; Chang, A.; Ferraro, R.; Xie, P.P.; Janowiak, J.; Schneider, U.; Curtis, S.; Bolvin, D.; Gruber, A.; Susskind, J.; Arkin, P.; Nelkin, E., 2003. The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis. Journal of Hydrometeorology, v. 4 (6), 1147-1167. https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2

Almazroui, M.; Islam, M.N., 2023. Spatiotemporal evaluation of five satellite-based precipitation products under the arid environment of Saudi Arabia. AIP Advances, v. 13 (4), 045222. https://doi.org/10.1063/5.0191924

Almeida, K.N.; Reis, J.A.T.; Buarque, B.C.; Mendonca, A.S.F.; Rodrigues, M.B.; Sa, G.L.N., 2020. Performance analysis of TRMM satellite in precipitation estimation for the Itapemirim River basin, Espirito Santo state, Brazil. Theoretical and Applied Climatology, v. 141, 791-802. https://doi.org/10.1007/s00704-020-03204-5

Bunde, A.; Ludescher, J.; Schellnhuber, H.J., 2024. Evaluation of the real-time El Niño forecasts by the climate network approach between 2011 and present. Theoretical and Applied Climatology, v. 155, 6727-6736. https://doi.org/10.1007/s00704-024-05035-0

Cai, W.; McPhaden, M.J.; Grimm, A.M.; Rodrigues, R.R.; Taschetto, A.S.; Garreaud, R.D.; Dewitte, B.; Poveda, G.; Ham, Y.-G.; Santoso, A.; Ng, B.; Anderson, W.; Wang, G.; Geng, T.; Jo, H.S.; Marengo, J.; Alves, L. M.; Osman, M.; Li, S.; Wu, L.; Karamperidou, C.; Takahashi, K.; Vera, C., 2020. Climate impacts of the El Niño-Southern Oscillation on South America. Nature Reviews Earth & Environment, v. 1, 215-231. https://doi.org/10.1038/s43017-020-0040-3

Centro de Previsão de Tempo e Estudos Climáticos, 2023. El Niño e La Niña (Accessed August 28, 2023). at: http://enos.cptec.inpe.br.

Chavda, D.; Li, J.; Farahmand, A., 2024. Assessing the influence of El Niño on the California precipitation regime during the satellite precipitation era. Hydrological Processes, v. 38 (5), e15160. https://doi.org/10.1002/hyp.15160

Chen, S.; Zhang, L.; Zhang, Y.; Guo, M.; Liu, X., 2020. Evaluation of Tropical Rainfall Measuring Mission (TRMM) satellite precipitation products for drought monitoring over the middle and lower reaches of the Yangtze River Basin, China. Journal of Geographical Sciences, v. 30, 53-67. https://doi.org/10.1007/s11442-020-1714-y

Collischonn, B.; Allasia, D.; Collischonn, W.; Tucci, C.E.M., 2007. Desempenho do satélite TRMM na estimativa de precipitação sobre a bacia do Paraguai superior. Revista Brasileira de Cartografia, v. 59 (1), 93-99. https://doi.org/10.14393/rbcv59n1-43965

Duarte, M.L.; Ribeiro, A., 2023. Influência do El Niño e La Niña na produtividade de plantios de Eucalipto em distintas regiões no Brasil. Ciência Florestal, v. 33 (1), e61334. https://doi.org/10.5902/1980509861334

Fan, N.; Lin, X.; Guo, H, 2023. An analysis for the applicability of global precipitation measurement mission (GPM) IMERG precipitation data in typhoons. Atmosphere, v. 14 (8), 1224. https://doi.org/10.3390/atmos14081224

Fan, Z.; Li, W.; Jiang, Q.; Sun, W.; Wen, J.; Gao, J., 2021. A comparative study of four merging approaches for regional precipitation estimation. IEEE Access, v. 9, 33625-33637. https://doi.org/10.1109/ACCESS.2021.3057057

Glantz, M.H.; Ramirez, I.J., 2020. Reviewing the Oceanic Niño Index (ONI) to enhance societal readiness for El Niño’s impacts. International Journal of Disaster Risk Science, v. 11, 394-403. https://doi.org/10.1007/s13753-020-00275-w

Guo, R.; Fan, X.; Zhou, H.; Liu, Y., 2024. Multi-sensor precipitation estimation from space: data sources, methods and validation. Remote Sensing, v. 16 (24), 4753. https://doi.org/10.3390/rs16244753

Haines, A.; Lam, H. C., 2023. El Niño and health in an era of unprecedented climate change. The Lancet, v. 402 (10415), 1811-1813. https://doi.org/10.1016/S0140-6736(23)01664-1

Huffman, G.J.; Adler, R.F.; Bolvin, D.T.; Gu, G.; Nelkin, E.J.; Bowman, K.P.; Wolff, D.B., 2007. The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology, v. 8 (1), 38-55. https://doi.org/10.1175/JHM560.1

Instituto de Pesquisa Econômica Aplicada (IPEA), 2021. Região Sudeste: Diagnóstico e perspectivas – Cadernos de Desenvolvimento Regional. Instituto de Pesquisa Econômica Aplicada, Brasília (Accessed August 11, 2025) at:. https://www.ipea.gov.br/portal/images/stories/PDFs/livros/livros/210223_cadernos_regiao_sudeste.pdf

Koralegedara, S.B.; Huang, W.R.; Tung, P.H.; Chiang, T.Y., 2023. El Niño‐Southern Oscillation modulation of springtime diurnal rainfall over a tropical Indian Ocean island. Earth and Space Science, v. 10 (5), e2023EA002832. https://doi.org/10.1029/2023EA002832

Kukulies, J.; Chen, D.; Wang, M., 2020. Temporal and spatial variations of convection, clouds and precipitation over the Tibetan Plateau from recent satellite observations. Part II: Precipitation climatology derived from global precipitation measurement mission. International Journal of Climatology, v. 40 (11), 4858-4875. https://doi.org/10.1002/joc.6493

Kummerow, C.; Simpson, J.; Thiele, O.; Barnes, W.; Chang, A.S.; Adler, R.; Olson, W.S., 2000. The status of the tropical rainfall measuring mission (TRMM) after two years in orbit. Journal of Applied Meteorology, v. 39 (12), 1965-1982. https://doi.org/10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2

Lal, D.; Singh, S., 2023. Impact of El-Nino and La-Nina episodes on rainfall variability and crop yield. International Journal of Environment and Climate Change, v. 13 (10), 2046-2051. https://doi.org/10.9734/IJECC/2023/v13i102865

Lee, J.H.; Julien, P.Y.; Cho, J.; Lee, S.; Kim, J.; Kang, W., 2023. Rainfall erosivity variability over the United States associated with large-scale climate variations by El Niño/southern oscillation. Catena, v. 226, 107050. https://doi.org/10.1016/j.catena.2023.107050

Li, Y.; Yi, F.; Wang, Y.; Gudaj, R., 2019. The Value of El Niño-Southern oscillation forecasts to China’s agriculture. Sustainability, v. 11 (15), 4184. https://doi.org/10.3390/su11154184

Louzada, F.L.D.O.; Xavier, A.C.; Pezzopane, J.E., 2018. Climatological water balance with data estimated by tropical rainfall measuring mission for the Doce river basin. Engenharia Agrícola, v. 38 (3), 376-386. https://doi.org/10.1590/1809-4430-Eng.Agric.v38n3p376-386/2018

Ma, Y.; Tang, G.; Long, D.; Yong, B.; Zhong, L.; Wan, W.; Hong, Y., 2016. Similarity and error intercomparison of the GPM and its predecessor-TRMM multisatellite precipitation analysis using the best available hourly gauge network over the Tibetan Plateau. Remote Sensing, v. 8 (7), 569. https://doi.org/10.3390/rs8070569

Macharia, J.M.; Ngetich, F.K.; Shisanya, C.A., 2020. Comparison of satellite remote sensing derived precipitation estimates and observed data in Kenya. Agricultural and Forest Meteorology, v. 284, 107875. https://doi.org/10.1016/j.agrformet.2019.107875

Medeiros, S.A.; Nóbrega, R.A.; Moraes Neto, J.M.; Barreto, B.B.; Vasconcelos, G.N.; Diniz, R.R.S., 2020. Investigação da influência do El Niño e da La Niña sobre a variabilidade da precipitação na cidade de Patos, Paraíba. Revista Brasileira de Geografia Física, v. 13 (1), 336-349. https://doi.org/10.26848/rbgf.v13.1.p336-349

Mekonnen, K.; Melesse, A.M.; Woldesenbet, T.A., 2021. Spatial evaluation of satellite-retrieved extreme rainfall rates in the Upper Awash River Basin, Ethiopia. Atmospheric Research, v. 249, 105297. https://doi.org/10.1016/j.atmosres.2020.105297

Mokhov, I.I., 2022. Changes in the frequency of phase transitions of different types of El Niño phenomena in recent decades. Izvestiya, Atmospheric and Oceanic Physics, v. 58 (1), 1-6. https://doi.org/10.1134/S000143382201008X

Montazeri, M.; Kiany, M.S.K.; Masoodian, S.A., 2020. Evaluation of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA v7) in drought monitoring over southwest Iran. Climate Research, v. 82, 55-73. https://doi.org/10.3354/cr01622

Nan, L.; Yang, M.; Wang, H.; Xiang, Z.; Hao, S., 2021. Comprehensive evaluation of global precipitation measurement mission (GPM) IMERG precipitation products over Mainland China. Water, v. 13 (23), 3381. https://doi.org/10.3390/w13233381

Pedreira Junior, A.L.; Querino, C.A.S.; Biudes, M.S.; Machado, N.G.; Santos, L.O.F.D.; Ivo, I.O., 2020. Influence of El Niño and La Niña phenomena on seasonality of the relative frequency of rainfall in southern Amazonas mesoregion. Revista Brasileira de Recursos Hídricos, v. 25, e24. https://doi.org/10.1590/2318-0331.252020190152

Raj, A.D.; Sooryamol, K.R.; Raj, A.D., 2021. Exploring temporal rainfall variability and trends over a tropical region using tropical rainfall measurement mission (TRMM) and observatory data. Hydrospatial Analysis, v. 5 (2), 56-71. https://doi.org/10.21523/gcj3.2021050202

Reboita, M.S.; Oliveira, K.R.; Corrêa, P.Y.C.; Rodrigues, R., 2021. Influência dos diferentes tipos do fenômeno El Niño na precipitação da América do Sul. Revista Brasileira de Geografia Física, v. 14 (2), 729-742. https://doi.org/10.26848/rbgf.v14.2.p729-742

Restrepo-Coupe, N.; Campos, K.S.; Alves, L.F.; Longo, M.; Wiedemann, K.T.; Oliveira, R.C.; Aragão, L.E.O.C.; Christoffersen, B.O.; Camargo, P.B.; Figueira, A.M.S.; Ferreira, M.L.; Oliveira, R.S.; Penha, D.; Prohaska, N.; Araujo, A.C.; Daube, B.C.; Wofsy, S.C.; Saleska, S.R., 2024. Contrasting carbon cycle responses to dry (2015 El Niño) and wet (2008 La Niña) extreme events at an Amazon tropical forest. Agricultural and Forest Meteorology, v. 353, 110037. https://doi.org/10.1016/j.agrformet.2024.110037

Retalis, A.; Katsanos, D.; Tymvios, F.; Michaelides, S., 2020. Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus. Remote Sensing, v. 12 (19), 3212. https://doi.org/10.3390/rs12193212

Santos, P.H.N.; Ferreira, W.S.; Santana, B.L.P., 2023. Repercussões do El Niño e La Niña na precipitação do estado de Sergipe – Brasil. Revista Brasileira de Climatologia, v. 33 (19), 409-437. https://doi.org/10.55761/abclima.v33i19.17395

Sharma, S.; Chen, Y.; Zhou, X.; Yang, K.; Li, X.; Niu, X.; Hu, X.; Khadka, N., 2020. Evaluation of GPM-Era satellite precipitation products on the southern slopes of the Central Himalayas against rain gauge data. Remote Sensing, v. 12 (11), 1836. https://doi.org/10.3390/rs12111836

Silva, A.S.A.; Stosic, B.; Menezes, R.S.C.; Singh, V.P., 2019. Comparison of interpolation methods for spatial distribution of monthly precipitation in the state of Pernambuco, Brazil. Journal of Hydrologic Engineering, v. 24 (3), 04018068. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001743

Silva, K.A.; Rolim, G.S.; Valeriano, T.T.B.; Moraes, J.R.S.C., 2020. Influence of El Niño and La Niña on coffee yield in the main coffee-producing regions of Brazil. Theoretical and Applied Climatology, v.139, 1019-1029. https://doi.org/10.1007/s00704-019-03039-9

Suroso, S.; Santoso, P.B.; Birkinshaw, S.; Kilsby, C.; Bárdossy, A.; Aldrian, E., 2023. Assessment of TRMM rainfall data for flood modelling in three contrasting catchments in Java, Indonesia. Journal of Hydroinformatics, v. 25 (3), 797-814. https://doi.org/10.2166/hydro.2023.132

Tan, M.L.; Duan, Z., 2017. Assessment of GPM and TRMM precipitation products over Singapore. Remote Sensing, v. 9 (7), 720. https://doi.org/10.3390/rs9070720

Tunas, I.G.; Herman, R.K.; Arafat, Y., 2024. Application of tropical rainfall measuring mission (TRMM) data for flood estimation in lack data catchment. IOP Conference Series: Earth and Environmental Science, v.1343, 012003. https://doi.org/10.1088/1755-1315/1343/1/012003

Woods, D.; Kirstetter, P.E.; Vergara, H.; Duarte, J.A.; Basara, J., 2023. Hydrologic evaluation of the global precipitation measurement mission over the US: Flood peak discharge and duration. Journal of Hydrology, v. 617 (Part C), 129124. https://doi.org/10.1016/j.jhydrol.2023.129124

Wu, D., 2024. History, mechanisms, and future directions of the El Niño-Southern oscillation under the severe climate change. Highlights in Science, Engineering and Technology, v. 88, 681-686. https://doi.org/10.54097/d1b2sb32

Yang, Y.; Tang, G.; Lei, X.; Hong, Y.; Yang, N., 2018. Can satellite precipitation products estimate probable maximum precipitation: A comparative investigation with gauge data in the Dadu River basin. Remote Sensing, v. 10 (1), 41. https://doi.org/10.3390/rs10010041

Zhao, Y.; Liu, Q.; Chen, L., 2023. Evaluation of GPM and TRMM and their capabilities for capturing solid and light precipitations in the headwater basin of the Heihe River. Atmosphere, v. 14 (3), 453. https://doi.org/10.3390/atmos14030453

Zhang, C.; Chen, X.; Shao, H.; Chen, S.; Liu, T.; Chen, C.; Ding, Q.; Du, H., 2018. Evaluation and intercomparison of high-resolution satellite precipitation estimates – GPM, TRMM, and CMORPH in the Tianshan Mountain Area. Remote Sensing, v. 10 (10), 1543. https://doi.org/10.3390/rs10101543