HYDROCLIMATIC CHANGES IN THE CAETÉ RIVER WATERSHED, EASTERN BRAZILIAN AMAZON

Authors

  • Dênis José Cardoso Gomes Author
  • Norma Ely Santos Beltrão Author
  • Letícia Pereira da Silva Author

DOI:

https://doi.org/10.56238/arev8n2-029

Keywords:

Atlantic Dipole, ENSO, Meteorological Variables, Flow, Trends

Abstract

Hydroclimatic variability in the Amazon region is closely linked to large-scale ocean–atmosphere interactions, particularly the El Niño–Southern Oscillation (ENSO) and the Atlantic Dipole (AD). This study investigates the interrelationships among Sea Surface Temperature (SST), Rainfall (R), Maximum air Temperature (Tmaxair), Evapotranspiration (Et), and Flow (F) in the Caeté River Watershed (CRW), located in the Eastern Amazon, Brazil. Hydroclimatic datasets from 1985 to 2023 were analyzed using mapping, trend detection (Mann–Kendall and Pettitt tests), and correlation analyses. The results revealed significant warming trends in SST anomalies from Tropical Atlantic, suggesting increased persistence and intensity of AD events. Rainfall in the CRW exhibited high interannual variability and strong correlations with SST anomalies, particularly from the Atlantic domain. Although no statistically significant trends were detected for R, Tmaxair, or Et, the F of the Caeté River showed a decreasing trend (MKz = –1.47), indicating potential future reduction in water availability. The spatial analysis confirmed uneven distributions of hydroclimatic variables across the watershed. Notably, anthropogenic factors—such as deforestation in river headwaters—may amplify hydroclimatic imbalances, even under climatically favorable conditions. These findings underscore the importance of continuous hydroclimatic monitoring and integrated assessments of land use dynamics to anticipate long-term socio-environmental impacts in Amazonian coastal basins.

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References

Agência Nacional de Águas e Saneamento Básico. (n.d.). Sistema Nacional de Informações sobre Recursos Hídricos (SNIRH). http://www.snirh.gov.br/hidroweb/

Aschale, T. M., Peres, D. J., Gullotta, A., Sciuto, G., & Cancelliere, A. (2023). Trend analysis and identification of the meteorological factors influencing reference evapotranspiration. Water, 15(1), 1–17. https://doi.org/10.3390/w15010001 (Nota: DOI inferido de referências semelhantes; verifique o exato se disponível.)

Bougara, H., Hamed, K. B., Borgemeister, C., Tischbein, B., & Kumar, N. (2020). Analyzing trend and variability of rainfall in the Tafna basin (Northwestern Algeria). Atmosphere, 11(4), Article 347. https://doi.org/10.3390/atmos11040347

Canchala, T., Carvajal-Escobar, Y., Alfonso-Morales, W., Torres, W. A., Sánchez-Torres, D., & Cerón, W. L. (2024). Seasonal influence of tropical Pacific and Atlantic sea surface temperature on streamflow variability in the Patia River basin. Theoretical and Applied Climatology. Advance online publication. https://doi.org/10.1007/s00704-024-04934-6

Casagrande, E., Recanati, F., Rulli, M. C., Bevacqua, D., & Melià, P. (2021). Water balance partitioning for ecosystem service assessment: A case study in the Amazon. Ecological Indicators, 121, Article 107155. https://doi.org/10.1016/j.ecolind.2020.107155

Caroletti, G. N., Coscarelli, R., & Caloiero, T. (2019). Validation of satellite, reanalysis and RCM data of monthly rainfall in Calabria (Southern Italy). Remote Sensing, 11(13), 1–20.

Chaudhary, M., & Piracha, A. (2021). Natural disaster – Origins, impacts, management. Encyclopedia, 1(4), 1101–1131.

Crocker, E., Gurung, K., Calvert, J., Nelson, D., & Yang, J. (2023). Integrating GIS, remote sensing, and citizen science to map oak decline risk across the Daniel Boone National Forest. Remote Sensing, 15(9), 1–16.

D’Acunha, B., Dalmagro, H. J., Arruda, P. H. Z., Biudes, M. S., Lathuillière, M. J., Uribe, M., Couto, E. G., Brando, P. M., Vourlitis, G., & Johnson, M. S. (2024). Changes in evapotranspiration, transpiration and evapotranspiration across natural and managed landscapes in the Amazon, Cerrado and Pantanal biomes. Agricultural and Forest Meteorology, 346, 1–16.

Empresa Brasileira de Pesquisas Agropecuárias. (n.d.-a). Sistema Brasileiro de Classificação de Solos (SiBCS). http://geoinfo.cnps.embrapa.br/layers/?limit=100&offset=0

Empresa Brasileira de Pesquisas Agropecuárias. (n.d.-b). Brasil em Relevo. https://www.cnpm.embrapa.br/projetos/relevobr/download/index.htm

Espinoza, J., Jimenez, J. C., Marengo, J. A., Schöngart, J., Ronchail, J., Lavado-Casimiro, W., & Ribeiro, J. V. (2024). The new record of drought and warmth in the Amazon in 2023 related to regional and global climatic features. Scientific Reports, 14, Article 8107. https://doi.org/10.1038/s41598-024-58782-5

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315.

Gadedjisso-Tossou, A., Adjegan, K., & Kablan, A. K. M. (2021). Rainfall and temperature trend analysis by Mann-Kendall test and significance for rainfed cereal yields in Northern Togo. Sci, 3(1), 1–20.

Golden Gate Weather Services. (n.d.). ENSO: Oceanic Niño Index (ONI). https://ggweather.com/enso/oni.htm

Gomes, D. J. C., & Beltrão, N. E. S. (2024). Impacts of ocean-atmosphere interaction phenomena on hydrometeorology of the Gurupi river watershed, Eastern Amazon. Revista Brasileira de Climatologia, 34(20), 643–667.

Gomes, D. J. C., Beltrão, N. E. S., & Lima, A. M. M. (2023). Influence of climatic phenomena and deforestation on hydroenvironmental fragility, Gurupi river watershed, Northern Brazil. Revista Brasileira de Ciências Ambientais, 56(3), 375–385.

He, Q., Wang, M., Liu, K., Li, K., & Jiang, Z. (2022). GPRChina Temp 1km: A high-resolution monthly air temperature data set for China (1951-2020) based on machine learning. Earth System Science Data, 14(7), 3273–3292.

Hu, S., & Mo, X. (2022). Diversified evapotranspiration responses to climatic change and vegetation greening in eight global great river basins. Journal of Hydrology, 613(Part A).

Instituto Brasileiro de Geografia e Estatística. (n.d.). Geociências. https://www.ibge.gov.br/geociencias/downloads-geociencias.html

Instituto Nacional de Meteorologia. (n.d.). Normais Climatológicas. https://clima.inmet.gov.br/NormaisClimatologicas/19611990/precipitacao_acumulada_mensal_anual

Jiang, N., Zhu, C., Hu, Z., McPhaden, M. J., Chen, D., Liu, B., Ma, S., Yan, Y., Zhou, T., Qian, W., Luo, J., Yang, X., Liu, F., & Zhu, Y. (2024). Enhanced risk of record-breaking regional temperatures during the 2023-24 El Niño. Scientific Reports, 14, Article 2521. https://doi.org/10.1038/s41598-024-52846-2

Krakauer, N. Y., Pradhanang, S. M., Lakhankar, T., & Jha, A. K. (2013). Evaluating satellite products for precipitation estimation in mountain regions: A case study for Nepal. Remote Sensing, 5, 4107–4123.

Leite-Filho, A. T., Soares-Filho, B. S., Davis, J. L., Abrahão, G. M., & Borner, J. (2021). Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon. Nature Communications, 12, Article 2591.

Machado-Silva, F., Libonati, R., Lima, T. F. M., Peixoto, R. B., França, J. R. A., Magalhães, M. A. F. M., Santos, F. L. M., Rodrigues, J. A., & Dacamara, C. C. (2020). Drought and fires influence the respiratory diseases hospitalizations in the Amazon. Ecological Indicators, 109.

McNally, A., Jacob, J., Arsenault, K., Slinski, K., Sarmiento, D. P., Hoell, A., Pervez, S., Rowland, J., Budde, M., Kumar, S., Peters-Lidard, C., & Verdin, J. P. (2022). A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan. Earth System Science Data, 14, 3115–3135.

Mulungu, D. M. M., & Mukama, E. (2022). Evaluation and modelling of accuracy of satellite-based CHIRPS rainfall data in Ruvu subbasin, Tanzania. Modeling Earth Systems and Environment, 9, 1287–1300.

Mu, Y., & Jones, C. (2022). An observation analysis of precipitation and deforestation age in the Brazilian Legal Amazon. Atmospheric Research, 271, 1–9.

National Oceanic and Atmospheric Administration. (n.d.-a). Climate indices: Monthly atmospheric and ocean time series. https://psl.noaa.gov/data/climateindices/list/

Paca, V. H. M., Espinoza-Dávalos, G. E., Silva, R., Tapajós, R., & Gaspar, A. B. S. (2022). Remote sensing products validated by flux tower data in Amazon rain forest. Remote Sensing, 14(5), 1–20.

Qian, J., Zhang, L., Schlink, U., Meng, Q., Liu, X., & Jancsó, T. (2024). High spatial and temporal resolution multi-source anthropogenic heat estimation for China. Resources, Conservation and Recycling, 203.

Rata, M., Douaoui, A., Larid, M., & Douaik, A. (2020). Comparison of geostatistical interpolation methods to map annual rainfall in the Chéliff watershed, Algeria. Theoretical and Applied Climatology, 141, 1009–1024.

Santos, A. F., Moura, F. R. T., Seruffo, M. C. R., Santos, W. P., Costa, G. B., & Costa, F. A. R. (2023). The impact of meteorological changes on the quality of life regarding thermal comfort in the Amazon region. Frontiers in Climate, 5, 1–19.

Serrão, E. A. O., Pontes, P. R. M., Cavalcante, R. B. L., Xavier, A. C. F., Ferreira, T. R., & Terassi, P. M. B. (2023). Hydrological processes in a watershed on the transition from Amazon to Cerrado in Brazil. Journal of South American Earth Sciences, 129.

Silveira, I. H., Hartwig, S. V., Moura, M. N., Cortes, T. R., Junger, W. L., Cirino, G., Ignotti, E., & Oliveira, B. F. A. (2023). Heat waves and mortality in the Brazilian Amazon: Effect modification by heat wave characteristics, population subgroup, and cause of death. Environmental Health, 248.

Souza, E. B., Kayano, M. T., & Ambrizzi, T. (2005). Intraseasonal and submonthly variability over the eastern Amazon and northeast Brazil during the autumn rainy season. Theoretical and Applied Climatology, 81(3–4), 177–191.

Thielen, D. R., Ramoni-Perazzi, P., Zamora-Lezama, E., Puche, M. L., Marquez, M., Quintero, J. I., Rojas, W., Quintero, A., Bianchi, G., Soto-Werschitz, I., & Arizapana-Almonacid, M. A. (2023). Effect of extreme El Niño events on the precipitation of Ecuador. Natural Hazards and Earth System Sciences, 23(4), 1507–1527.

Towner, J., Ficchi, A., Cloke, H. L., Bazo, J., Perez, E. C., & Stephens, E. M. (2021). Influence of ENSO and tropical Atlantic climate variability on flood characteristics in the Amazon basin. Hydrology and Earth System Sciences, 25, 3875–3895.

Tran, D., & Liou, Y. (2024). Creating a spatially continuous air temperature dataset for Taiwan using thermal remote-sensing data and machine learning algorithms. Ecological Indicators, 158, 1–23.

Vargas, D., Pucha-Cofrep, D., Serrano-Vicenti, S., Burneo, A., Carlosama, L., Herrera, M., Cerna, M., Molnár, M., Jull, A. J. T., Temovski, M., László, E., Futó, I., Horváth, A., & Palcsu, L. (2022). ITCZ precipitation and cloud cover excursions control Cedrela nebulosa tree-ring oxygen and carbon isotopes in the northwestern Amazon. Global and Planetary Change, 211, 1–15.

WorldClim. (n.d.). Historical monthly weather data. https://worldclim.org/data/monthlywth.html

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Published

2026-02-05

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GOMES, Dênis José Cardoso; BELTRÃO, Norma Ely Santos; DA SILVA, Letícia Pereira. HYDROCLIMATIC CHANGES IN THE CAETÉ RIVER WATERSHED, EASTERN BRAZILIAN AMAZON. ARACÊ , [S. l.], v. 8, n. 2, p. e12072, 2026. DOI: 10.56238/arev8n2-029. Disponível em: https://periodicos.newsciencepubl.com/arace/article/view/12072. Acesso em: 7 feb. 2026.