Variabilidad del caudal del río Neuquén en las fases de su ciclo anual y su relación con índices climáticos

Lorenzo Ricetti; Santiago I. Hurtado; Eduardo Agosta Scarel; Andrés Cesanelli

doi:10.24215/1850468Xe026

Authors

Lorenzo Ricetti Universidad Nacional de La Plata, Consejo Nacional de Investigaciones Cientı́ficas y Técnicas (CONICET), Argentina
Santiago I. Hurtado Instituto Nacional de Tecnología Agropecuaria, Consejo Nacional de Investigaciones Científicas y T´ecnicas (CONICET), Argentina
Eduardo Agosta Scarel Equipo Interdisciplinario para el Estudio de Procesos Atmosféricos en el Cambio Global, Pontificia Universidad Católica Argentina, Argentina
Andrés Cesanelli Universidad Nacional de La Plata, Argentina

DOI:

https://doi.org/10.24215/1850468Xe026

Keywords:

Northern Patagonia, El Niño Southern Oscillation, Southern Annular Mode, Indian Ocean Dipole, Atlantic Ocean

Abstract

The Neuquén river has great relevance for the northern Patagonian region. For this reason, this work aims to study it streamflow variability based on observational data in the context of hydrological emergency in the basin. Firstly, a study of daily streamflow infilling methods was performed, from which the multiple linear regression stood out as the most appropriate for the basin. Then, the phases of the annual cycle were determined with an objective methodology. The methodology was able to determine the beginning and ending dates in each of the three identified phases, in concordance with the river’s pluvio-nival cycle. Accordingly, there is a minimum streamflow phase, which takes place from the beginning of the year until May, a relative maximum phase which extends from May through mid-September, when the absolute maximum phase begins until the next minimum phase. Subsequently, streamflow series representative of each phase were examined. All the estimated series showed a breakpoint or step change towards lower streamflow between 2007 and 2010, which induces negative and significant trends, yet spurious. Throughout the homogeneous period before the breakpoint, the series of the different phases showed distinct variability. Regarding the potential forcings, the streamflow of the minimum phase showed an inverse relationship with the Southern Annular Mode (SAM) index and a direct association with the TNA index, which represents the sea surface temperature (SST) variability of the tropical North Atlantic Ocean. The streamflow of the relative maximum phase exhibited a direct relationship with El Niño Southern Oscillation (ENSO) indices and an inverse association with the TSA index of the tropical south Atlantic SST. Lastly, the streamflow of the absolute maximum phase showed a direct relationship with the ENSO and Indian Ocean Dipole indices and an inverse connection with the SAM and TNA indices. These results provide useful insights about the changes in the hydrological regime of the river and its variability, which is relevant in the management of the resource.

Downloads

Download data is not yet available.

References

Aguayo, R., León-Muñoz, J., Vargas-Baecheler, J., Montecinos, A., Garreaud, R., Urbina, M., ... & Iriarte, J. L. (2019). The glass half-empty: climate change drives lower freshwater input in the coastal system of the Chilean Northern Patagonia. Climatic Change, 155, 417-435.

Alfieri, L., Lorini, V., Hirpa, F. A., Harrigan, S., Zsoter, E., Prudhomme, C., & Salamon, P. (2020). A global streamflow reanalysis for 1980–2018. Journal of Hydrology X, 6, 100049.

Araneo, D. C., & Compagnucci, R. H. (2008). Atmospheric circulation features associated to Argentinean Andean rivers discharge variability. Geophysical Research Letters, 35(1).

Bais, F. M. (2017). Informe técnico final. Caracterización de sequías hidrológicas en cuencas de la patagonia de la república argentina. Facultad de Ciencias exactas, físicas y naturales, Universidad Nacional de Córdoba, p36. https://rdu.unc.edu.ar/handle/11086/5100.

Behera, S. K., & Yamagata, T. (2003). Influence of the Indian Ocean dipole on the Southern Oscillation. Journal of the Meteorological Society of Japan. Ser. II, 81(1), 169-177

Berri, G. J., Bianchi, E., & Müller, G. V. (2019). El Niño and La Niña influence on mean river flows of southern South America in the 20th century. Hydrological Sciences Journal, 64(8), 900-909.

Braun, M. H., Malz, P., Sommer, C., Farías-Barahona, D., Sauter, T., Casassa, G., ... & Seehaus, T. C. (2019). Constraining glacier elevation and mass changes in South America. Nature Climate Change, 9(2), 130-136.

Campitelli, E. (2018). metr-visualización y manejo de datos meteorológicos. In Conferencia Latinoamericana sobre Uso de R en Investigación+Desarrollo (LatinR 2018)-JAIIO 47 (CABA, 2018).

Carvalho, L. M., Jones, C., & Ambrizzi, T. (2005). Opposite phases of the Antarctic Oscillation and relationships with intraseasonal to interannual activity in the tropics during the austral summer. Journal of climate, 18(5), 702-718.

Cerrudo, C. G., Díaz, G. M., Juárez, S. H., & Ferreira, L. J. (2017). Análisis de la relación espacio temporal entre la precipitación estimada por el satélite TRMM (3B42RT) y el caudal medio diario en la cuenca del Río Iguazú. Meteorológica, 42(1), 39-52.

Chan, S. C., Behera, S. K., & Yamagata, T. (2008). Indian Ocean dipole influence on South American rainfall. Geophysical Research Letters, 35(14).

Compagnucci, R. H., & Araneo, D. C. (2007). Alcances de El Niño como predictor del caudal de los ríos andinos argentinos. Ingeniería hidráulica en México, 22(3), 23-35.

Cordero, R. R., Asencio, V., Feron, S., Damiani, A., Llanillo, P. J., Sepulveda, E., ... & Casassa, G. (2019). Dry-season snow cover losses in the Andes (18–40 S) driven by changes in large-scale climate modes. Scientific Reports, 9(1), 16945.

Enfield, D. B., Mestas‐Nuñez, A. M., Mayer, D. A., & Cid‐Serrano, L. (1999). How ubiquitous is the dipole relationship in tropical Atlantic sea surface temperatures?. Journal of Geophysical Research: Oceans, 104(C4), 7841-7848.

Finessi, F. G. & Groch, D. (2018). Tesis de licenciatura. Estudio hidrológico de la cuenca alta del río Neuquén. Facultad de Humanidades, Universidad Nacional del Comahue, p19. http://rdi.uncoma.edu.ar/bitstream/handle/uncomaid/5825/Tesis%20Finessi%20&%20Groch%20(2018).pdf?sequence=1.

Fogt, R. L., & Marshall, G. J. (2020). The Southern Annular Mode: variability, trends, and climate impacts across the Southern Hemisphere. Wiley Interdisciplinary Reviews: Climate Change, 11(4), e652.

Garreaud, R. D., Alvarez-Garreton, C., Barichivich, J., Boisier, J. P., Christie, D., Galleguillos, M., ... & Zambrano-Bigiarini, M. (2017). The 2010–2015 megadrought in central Chile: Impacts on regional hydroclimate and vegetation. Hydrology and earth system sciences, 21(12), 6307-6327.

González, M. H., & Vera, C. S. (2010). On the interannual wintertime rainfall variability in the Southern Andes. International Journal of Climatology: A Journal of the Royal Meteorological Society, 30(5), 643-657.

Grolemund, G. & Wickham, H. (2011). Dates and times made easy with lubridate. Journal of statistical software, 40(1), 1–25.

Hamzah, F. B., Mohd Hamzah, F., Mohd Razali, S. F., Jaafar, O., & Abdul Jamil, N. (2020). Imputation methods for recovering streamflow observation: A methodological review. Cogent Environmental Science, 6(1), 1745133.

Hipel, K. W., & McLeod, A. I. (1994). Time series modelling of water resources and environmental systems. Elsevier.

Hurtado, S. I., Zaninelli, P. G., & Agosta, E. A. (2020). A multi-breakpoint methodology to detect changes in climatic time series. An application to wet season precipitation in subtropical Argentina. Atmospheric Research, 241, 104955.

Hurtado, S. I., Zaninelli, P. G., Agosta, E. A., & Ricetti, L. (2021). Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina. Atmospheric Research, 254, 105482.

Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of statistical software, 27, 1-22.

Ismail, W. N. W., Zin, W. Z. W., & Ibrahim, W. (2017). Estimation of rainfall and stream flow missing data for Terengganu, Malaysia by using interpolation technique methods. Malaysian Journal of Fundamental and Applied Sciences, 13(3), 213-217.

Labat, D. (2010). Cross wavelet analyses of annual continental freshwater discharge and selected climate indices. Journal of Hydrology, 385(1-4), 269-278.

Lauro, C., Vich, A., & Moreiras, S. M. (2015). Variabilidad del régimen fluvial en cuencas de la región de Cuyo. Geoacta, 40(2), 28-51.

Lauro, C., Vich, A. I., & Moreiras, S. M. (2019). Streamflow variability and its relationship with climate indices in western rivers of Argentina. Hydrological Sciences Journal, 64(5), 607-619.

Li, J., & Heap, A. D. (2011). A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics, 6(3-4), 228-241.

Maraun, D., & Kurths, J. (2004). Cross wavelet analysis: significance testing and pitfalls. Nonlinear Processes in Geophysics, 11(4), 505-514.

Marshall, G. J. (2003). Trends in the Southern Annular Mode from observations and reanalyses. Journal of climate, 16(24), 4134-4143.

Masiokas, M. H., Villalba, R., Luckman, B. H., Lascano, M. E., Delgado, S., & Stepanek, P. (2008). 20th-century glacier recession and regional hydroclimatic changes in northwestern patagonia. Global and Planetary Change, 60(1-2), 85–100.

Masiokas, M. H., Cara, L., Villalba, R., Pitte, P., Luckman, B. H., Toum, E., ... & Mauget, S. (2019). Streamflow variations across the Andes (18–55 S) during the instrumental era. Scientific Reports, 9(1), 17879.

Ng, W. W., Panu, U. S., & Lennox, W. C. (2009). Comparative studies in problems of missing extreme daily streamflow records. Journal of Hydrologic Engineering, 14(1), 91-100.

Organismo Regulador de Seguridad de Presas - ORSEP (2020). Diques argentinos en realidad aumentada. Secretaria de Infraestructura y Política Hídrica. https://www.argentina.gob.ar/sites/default/files/orsep-diques_argentinos_en_ra.pdf

Pessacg N, Flaherty S, Solman S, Pascual M (2020) Climate change in Northern Patagonia: critical decrease in water resources. Theoretical and Applied Climatology (140), 807–822 .

Pohlert, T., Pohlert, M. T., & Kendall, S. (2016). Package ‘trend’. Title Non-Parametric Trend Tests and Change-Point Detection. Available at: https://cran.r-project.org/package=trend.

R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Raggio, G. A., & Saurral, R. I. (2021). Probable intensificación de las condiciones de déficit hídrico sobre la región del Comahue ante diversos escenarios de Cambio Climático. Meteorológica; 46; 1; 6-2021; 48-71

Reboita, M. S., Ambrizzi, T., & Rocha, R. P. D. (2009). Relationship between the southern annular mode and southern hemisphere atmospheric systems. Revista Brasileira de Meteorológica, 24, 48-55.

Reboita, M. S., Ambrizzi, T., Crespo, N. M., Dutra, L. M. M., Ferreira, G. W. D. S., Rehbein, A., ... & Souza, C. A. D. (2021). Impacts of teleconnection patterns on South America climate. Annals of the New York Academy of Sciences, 1504(1), 116-153.

Rivera, J. A., Araneo, D. C., Penalba, O. C., & Villalba, R. (2018). Regional aspects of streamflow droughts in the Andean rivers of Patagonia, Argentina. Links with large-scale climatic oscillations. Hydrology Research, 49(1), 134-149.

Rivera, J. A., Otta, S., Lauro, C., & Zazulie, N. (2021). A decade of hydrological drought in Central-Western Argentina. Frontiers in Water, 3, 640544.

Romero, P. E., & González, M. H. (2016). Relación entre caudales y precipitación en algunas cuencas de la Patagonia norte. Revista de Geología Aplicada a la Ingeniería y al Ambiente, (36), 7-13.

Romero, P. E., Gabarini E. M., González M. H. (2014). Características hídricas y climáticas del norte Patagónico. Tagliavini et al., eds. II Encuentro de investigadores en formación en Recursos Hídricos, 9-10.

Saavedra, F. A., Kampf, S. K., Fassnacht, S. R., & Sibold, J. S. (2018). Changes in Andes snow cover from MODIS data, 2000–2016. The Cryosphere, 12(3), 1027-1046.

Sammut, C., & Webb, G. I. (2010). Leave-one-out cross-validation. Encyclopedia of machine learning. Springer, Boston, MA, pags 600-6001.

Saplioglu, K., & Kucukerdem, T. S. (2018). Estimation of missing streamflow data using ANFIS models and determination of the number of datasets for ANFIS: the case of Yeşilırmak River.

Scarpati, O. E., Spescha, L., Fioriti, M. J., & Capriolo, A. D. (2001). El niño driven climate variability and drainage anomalies in Patagonian region Argentina. Cuadernos de Investigación Geográfica, 27, 179-191.

Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American statistical association, 63(324), 1379-1389.

Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological society, 79(1), 61-78.

Valdés-Pineda, R., García-Chevesich, P., Valdés, J. B., & Pizarro-Tapia, R. (2020). The first drying lake in Chile: causes and recovery options. Water, 12(1), 290.

Vich, A. I. J., Bizzotto, F., Vaccarino, E., Correas, M., Manduca, F., Paoli, C. U., & Malinow, G. V. (2010). Tendencias y cambios abruptos en el escurrimiento de algunos ríos con nacientes en la cordillera y serranías del oeste argentino. Criterios para la determinación de crecidas de diseño en sistemas climáticos cambiantes. Carlos Ubaldo Paoli et al. 1a ed.-Santa Fe: Universidad Nacional del Litoral, 149-166.

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., et al. (2019). Welcome to the tidyverse. Journal of open source software, 4(43), 1686.