Analysis of streamflow variability and trends in the Meta River, Colombia

The aim of this research is the detection and analysis of existing trends in the Meta River, Colombia, based on the streamflow records from seven gauging stations in its main course, for the period between June 1983 to July 2019. The Meta River is one of the principal branches of the Orinoco River,...

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Autores:
Arrieta-Castro, Marco
Donado-Rodríguez, Adriana
Acuña Robles, Guillermo Jesús
Canales, Fausto
Teegavarapu, Ramesh S. V.
Kázmierczak, Bartosz
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/6796
Acceso en línea:
https://hdl.handle.net/11323/6796
https://repositorio.cuc.edu.co/
Palabra clave:
Streamflow trends
Mann–Kendall
Modified Mann-Kendall
Sens’s slope
Meta River
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License
CC0 1.0 Universal
id RCUC2_c28d575573215d8a04295e5a4722bdc6
oai_identifier_str oai:repositorio.cuc.edu.co:11323/6796
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Analysis of streamflow variability and trends in the Meta River, Colombia
title Analysis of streamflow variability and trends in the Meta River, Colombia
spellingShingle Analysis of streamflow variability and trends in the Meta River, Colombia
Streamflow trends
Mann–Kendall
Modified Mann-Kendall
Sens’s slope
Meta River
title_short Analysis of streamflow variability and trends in the Meta River, Colombia
title_full Analysis of streamflow variability and trends in the Meta River, Colombia
title_fullStr Analysis of streamflow variability and trends in the Meta River, Colombia
title_full_unstemmed Analysis of streamflow variability and trends in the Meta River, Colombia
title_sort Analysis of streamflow variability and trends in the Meta River, Colombia
dc.creator.fl_str_mv Arrieta-Castro, Marco
Donado-Rodríguez, Adriana
Acuña Robles, Guillermo Jesús
Canales, Fausto
Teegavarapu, Ramesh S. V.
Kázmierczak, Bartosz
dc.contributor.author.spa.fl_str_mv Arrieta-Castro, Marco
Donado-Rodríguez, Adriana
Acuña Robles, Guillermo Jesús
Canales, Fausto
Teegavarapu, Ramesh S. V.
Kázmierczak, Bartosz
dc.subject.spa.fl_str_mv Streamflow trends
Mann–Kendall
Modified Mann-Kendall
Sens’s slope
Meta River
topic Streamflow trends
Mann–Kendall
Modified Mann-Kendall
Sens’s slope
Meta River
description The aim of this research is the detection and analysis of existing trends in the Meta River, Colombia, based on the streamflow records from seven gauging stations in its main course, for the period between June 1983 to July 2019. The Meta River is one of the principal branches of the Orinoco River, and it has a high environmental and economic value for this South American country. The methods employed for the trend detection and quantification were the Mann–Kendall (MK) test, the modified MK (MMK) test, and the Sen’s slope (SS) estimator. Statistically significant trends (at a 95% level of confidence) were detected in more than 30% of the 105 evaluated datasets. The results from the MK test indicate the presence of statistically significant downward trends in the upstream stations and upward trends in the downstream stations, with the latter presenting steep positive slopes. The findings of this study are valuable assets for water resources management and sustainable planning in the Meta River Basin.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-07-22T16:05:23Z
dc.date.available.none.fl_str_mv 2020-07-22T16:05:23Z
dc.date.issued.none.fl_str_mv 2020-05-20
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.identifier.issn.spa.fl_str_mv 2073-4441
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/6796
dc.identifier.doi.spa.fl_str_mv doi:10.3390/w12051451
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 2073-4441
doi:10.3390/w12051451
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/6796
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Grizzetti, B.; Lanzanova, D.; Liquete, C.; Reynaud, A.; Cardoso, A.C. Assessing water ecosystem services for water resource management. Environ. Sci. Policy 2016, 61, 194–203. [CrossRef]
2. Silva, A.T. Introduction to Nonstationary Analysis and Modeling of Hydrologic Variables. In Fundamentals of Statistical Hydrology; Naghettini, M., Ed.; Springer International Publishing: Cham, Switzerland, 2017; pp. 537–577.
3. Arfan, M.; Lund, J.; Hassan, D.; Saleem, M.; Ahmad, A. Assessment of Spatial and Temporal Flow Variability of the Indus River. Resources 2019, 8, 103. [CrossRef]
4. Ali, R.; Ismael, A.; Heryansyah, A.; Nawaz, N. Long Term Historic Changes in the Flow of Lesser Zab River, Iraq. Hydrology 2019, 6, 22. [CrossRef]
5. Tadese, M.T.; Kumar, L.; Koech, R.; Zemadim, B. Hydro-Climatic Variability: A Characterisation and Trend Study of the Awash River Basin, Ethiopia. Hydrology 2019, 6, 35. [CrossRef]
6. Langat, P.; Kumar, L.; Koech, R. Temporal Variability and Trends of Rainfall and Streamflow in Tana River Basin, Kenya. Sustainability 2017, 9, 1963. [CrossRef]
7. Yilmaz, A.G.; Perera, B.J.C. Spatiotemporal Trend Analysis of Extreme Rainfall Events in Victoria, Australia. Water Resour. Manag. 2015, 29, 4465–4480. [CrossRef]
8. Wdowikowski, M.; Ka ´zmierczak, B.; Ledvinka, O. Maximum daily rainfall analysis at selected meteorological stations in the upper Lusatian Neisse River basin. Meteorol. Hydrol. Water Manag. 2016, 4, 53–63. [CrossRef]
9. Pellicciotti, F.; Burlando, P.; Van Vliet, K. Recent trends in precipitation and streamflow in the Aconcagua River Basin, central Chile. In IAHS Assembly; IAHS Publ.: Foz do Iguaçu, Brazil, 2007; pp. 1–22.
10. Su, L.; Miao, C.; Kong, D.; Duan, Q.; Lei, X.; Hou, Q.; Li, H. Long-term trends in global river flow and the causal relationships between river flow and ocean signals. J. Hydrol. 2018, 563, 818–833. [CrossRef]
11. Feng, J.; Li, D.; Wang, T.; Liu, Q.; Deng, L.; Zhao, L. Acceleration of the Extreme Sea Level Rise Along the Chinese Coast. Earth Space Sci. 2019, 6, 1942–1956. [CrossRef]
12. Teegavarapu, R.S.V. Methods for Analysis of Trends and Changes in Hydroclimatological Time-Series. In Trends and Changes in Hydroclimatic Variables; Teegavarapu, R.S.V., Ed.; Elsevier: Amsterdam, The Netherlands, 2019; pp. 1–89.
13. ¸Sener, ¸S.; ¸Sener, E.; Davraz, A. Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey). Sci. Total Environ. 2017, 584–585, 131–144. [CrossRef]
14. Ndehedehe, C.E.; Ferreira, V.G. Assessing land water storage dynamics over South America. J. Hydrol. 2020, 580, 124339. [CrossRef]
15. Vega-Jácome, F.; Lavado-Casimiro, W.S.; Felipe-Obando, O.G. Assessing hydrological changes in a regulated river system over the last 90 years in Rimac Basin (Peru). Theor. Appl. Climatol. 2018, 132, 347–362. [CrossRef]
16. Morán-Tejeda, E.; Bazo, J.; López-Moreno, J.I.; Aguilar, E.; Azorín-Molina, C.; Sanchez-Lorenzo, A.; Martínez, R.; Nieto, J.J.; Mejía, R.; Martín-Hernández, N.; et al. Climate trends and variability in Ecuador (1966–2011). Int. J. Climatol. 2016, 36, 3839–3855. [CrossRef]
17. Seoane, R.; López, P. Assessing the effects of climate change on the hydrological regime of the Limay River basin. GeoJournal 2007, 70, 251–256. [CrossRef]
18. Vich, A.I.J.; López, P.M.; Schumacher, M.C. Trend detection in the water regime of the main rivers of the Province of Mendoza, Argentina. GeoJournal 2007, 70, 233–243. [CrossRef]
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20. De Alcântara, L.R.P.; de Costa, I.R.A.; de Barros, V.H.O.; Santos Neto, S.M.; Coutinho, A.P.; Antonino, A.C.D. Análise de tendência para dados pluviométricos no município de Toritama-PE. J. Environ. Anal. Prog. 2019, 4, 130. [CrossRef]
21. De Quadros, L.E.; de Mello, E.L.; Gomes, B.M.; Araujo, F.C. Rainfall trends for the State of Paraná: Present and future climate. Ambient. Agua Interdiscip. J. Appl. Sci. 2019, 14, 1. [CrossRef]
22. Rodrigues, A.L.M.; Reis, G.B.; dos Santos, M.T.; da Silva, D.D.; dos Santos, V.J.; de Siqueira Castro, J.; Calijuri, M.L. Influence of land use and land cover’s change on the hydrological regime at a Brazilian southeast urbanized watershed. Environ. Earth Sci. 2019, 78, 595. [CrossRef]
23. De Santos, V.O.; de Naves, J.G.P. Low flow diagnosis on upper course of Uberaba river basin. Ambiência 2016, 12, 859–868. [CrossRef]
24. Trindade, A.L.C.; de Almeida, K.C.B.; Barbosa, P.E.; Oliveira, S.M.A.C. Tendências temporais e espaciais da qualidade das águas superficiais da sub-bacia do Rio das Velhas, estado de Minas Gerais. Eng. Sanit. Ambient. 2016, 22, 13–24. [CrossRef]
25. Restrepo, J.C.; Ortíz, J.C.; Maza, M.; Otero, L.; Alvarado, M.; Aguirre, J. Estimating fluvial discharge in the Caribbean seaboard of Colombia: Magnitude, variability and extreme events. Coast. Eng. Proc. 2012, 1, 44. [CrossRef]
26. Restrepo, J.C.; Ortíz, J.C.; Pierini, J.; Schrottke, K.; Maza, M.; Otero, L.; Aguirre, J. Freshwater discharge into the Caribbean Sea from the rivers of Northwestern South America (Colombia): Magnitude, variability and recent changes. J. Hydrol. 2014, 509, 266–281. [CrossRef]
27. Restrepo, J.D.; Escobar, H.A. Sediment load trends in the Magdalena River basin (1980–2010): Anthropogenic and climate-induced causes. Geomorphology 2018, 302, 76–91. [CrossRef]
28. Restrepo, J.D.; Escobar, R.; Tosic, M. Fluvial fluxes from the Magdalena River into Cartagena Bay, Caribbean Colombia: Trends, future scenarios, and connections with upstream human impacts. Geomorphology 2018, 302, 92–105. [CrossRef]
29. Ávila, Á.; Guerrero, F.; Escobar, Y.; Justino, F. Recent Precipitation Trends and Floods in the Colombian Andes. Water 2019, 11, 379. [CrossRef]
30. Puertas-Orozco, O.L.; Carvajal-Escobar, Y.; Quintero-Angel, M. Estudio de tendencias de la precipitación mensual en la cuenca alta-media del río Cauca, Colombia. DYNA 2011, 78, 112–120.
31. Carmona, A.M.; Poveda, G. Detection of long-term trends in monthly hydro-climatic series of Colombia through Empirical Mode Decomposition. Clim. Change 2014, 123, 301–313. [CrossRef]
32. Ardila Luna, D.C. El río Meta y los proyectos para la integración de los Llanos Orientales colombianos, desde la Colonia hasta el siglo XXI. Anu. Hist. Reg. Front. 2016, 21, 265–283.
33. Ministerio de Transporte Plan Maestro Fluvial de Colombia 2015. 2015; 108. Available online: https://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved= 2ahUKEwi7x8zh4MHpAhVNCqYKHQb1C6EQFjAAegQIAxAB&url=https%3A%2F%2Fwww. mintransporte.gov.co%2Fdescargar.php%3FidFile%3D13276&usg=AOvVaw2Eky0KTUsR1_G0JSnb7I_1 (accessed on 15 May 2020).
34. Liu, L.; Wen, Y.; Liang, Y.; Zhang, F.; Yang, T. Extreme weather impacts on inland waterways transport of Yangtze River. Atmosphere (Basel) 2019, 10, 133. [CrossRef]
35. Actualización de los Estudios y Diseños para la Navegabilidad del río Meta entre Cabuyaro (K804) y Puerto Carreño (K0); Universidad del Norte: Barranquilla, Colombia, 2013.
36. Acuña, G.J.; Ávila, H.; Canales, F.A. River Model Calibration Based on Design of Experiments Theory. A Case Study: Meta River, Colombia. Water 2019, 11, 1382. [CrossRef]
37. Warne, A.G.; Meade, R.H.; White, W.A.; Guevara, E.H.; Gibeaut, J.; Smyth, R.C.; Aslan, A.; Tremblay, T. Regional controls on geomorphology, hydrology, and ecosystem integrity in the Orinoco Delta, Venezuela. Geomorphology 2002, 44, 273–307. [CrossRef]
38. IDEAM. Estudio Nacional del Agua; Instituto de Hidrología, Meteorología y Estudios Ambientales: Bogotá DC, Colombia, 2010.
39. IDEAM. Consulta y Descarga de Datos Hidrometeorológicos. Available online: http://dhime.ideam.gov.co/atencionciudadano/ (accessed on 10 October 2019).
40. Umar, D.A.; Ramli, M.F.; Aris, A.Z.; Jamil, N.R.; Aderemi, A.A. Evidence of climate variability from rainfall and temperature fluctuations in semi-arid region of the tropics. Atmos. Res. 2019, 224, 52–64. [CrossRef]
41. Salman, S.A.; Shahid, S.; Ismail, T.; Chung, E.-S.; Al-Abadi, A.M. Long-term trends in daily temperature extremes in Iraq. Atmos. Res. 2017, 198, 97–107. [CrossRef]
42. Hamed, K.H.; Rao, A.R. A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol. 1998, 204, 182–196. [CrossRef]
43. Kibria, K.N.; Ahiablame, L.; Hay, C.; Djira, G. Streamflow trends and responses to climate variability and land cover change in South Dakota. Hydrology 2016, 3, 2. [CrossRef]
44. Yue, S.; Pilon, P.; Phinney, B.; Cavadias, G. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol. Process. 2002, 16, 1807–1829. [CrossRef]
45. Winslow, L.A.; Read, J.S.; Hansen, G.J.A.; Hanson, P.C. Small lakes show muted climate change signal in deepwater temperatures. Geophys. Res. Lett. 2015, 42, 355–361. [CrossRef]
46. Mohd Razali, N.; Bee Wah, Y. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. J. Stat. Model. Anal. 2011, 2, 21–33.
47. Lozano Blanco, L.J.; Meseguer Zapata, V.F.; De Juan García, D. Statistical analysis of laboratory results of Zn wastes leaching. Hydrometallurgy 1999, 54, 41–48. [CrossRef]
48. Ahmad, I.; Tang, D.; Wang, T.; Wang, M.; Wagan, B. Precipitation trends over time using Mann-Kendall and spearman’s Rho tests in swat river basin, Pakistan. Adv. Meteorol. 2015, 2015, 15. [CrossRef]
49. Pohlert, T. Package “Trend”: Non-Parametric Trend Tests and Change-Point Detection. Available online: https://cran.r-project.org/package=trend (accessed on 3 March 2020).
50. Xu, S.; Qin, M.; Ding, S.; Zhao, Q.; Liu, H.; Li, C.; Yang, X.; Li, Y.; Yang, J.; Ji, X. The impacts of climate variation and land use changes on streamflow in the Yihe River, China. Water (Switzerland) 2019, 11, 887. [CrossRef]
51. Guarín Giraldo, G.W.; Poveda, G. Variabilidad espacial y temporal del almacenamiento de agua en el suelo en Colombia. Rev. Acad. Colomb. Ciencias Exactas Físicas Nat. 2013, 37, 89–113.
52. Estudio Nacional del Agua 2018; IDEAM: Bogota, Colombia, 2019.
53. Guse, B.; Kail, J.; Radinger, J.; Schröder, M.; Kiesel, J.; Hering, D.; Wolter, C.; Fohrer, N. Eco-hydrologic model cascades: Simulating land use and climate change impacts on hydrology, hydraulics and habitats for fish and macroinvertebrates. Sci. Total Environ. 2015, 533, 542–556. [CrossRef]
54. IDEAM. Leyenda Nacional de Coberturas de la Tierra. Metodología CORINE Land Cover Adaptada para Colombia Escala 1:100.000; Instituto de Hidrología, Meteorología y Estudios Ambientales: Bogota, DC, Colombia, 2010.
55. Armenteras, D.; González, T.; Meza, M.; Ramírez-Delgado, J.P.; Cabrera, E.; Galindo, G.; Yepes, A. Causas de Degradación Forestal en Colombia: Una Primera Aproximación; Universidad Nacional de Colombia Sede Bogotá, Instituto de Hidrología, Meteorología y Estudios Ambientales de Colombia-IDEAM, Programa ONU-REDD: Bogota, DC, Colombia, 2018.
56. Food and Agriculture Organization of the United Nations FAOSTAT. Available online: http://www.fao.org/faostat/en/#data/LC/visualize (accessed on 20 April 2020).
57. IDEAM. Informe Anual Sobre el Estado del Medio Ambiente y los Recursos Naturales Renovables de Colombia; Imprenta Nacional: Bogota, Colombia, 2004.
58. Torres Pungo, K.; Rivera, H.G.; Rivera, M.; Fuentes Bacca, J.B.; León Alvarez, M.A. Identificación de la incertidumbre en el proceso estocástico de caudales medios en el río Fonce (San Gil–Santander). Av. Investig. Ing. 2015, 12, 1. [CrossRef
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spelling Arrieta-Castro, MarcoDonado-Rodríguez, AdrianaAcuña Robles, Guillermo JesúsCanales, FaustoTeegavarapu, Ramesh S. V.Kázmierczak, Bartosz2020-07-22T16:05:23Z2020-07-22T16:05:23Z2020-05-202073-4441https://hdl.handle.net/11323/6796doi:10.3390/w12051451Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The aim of this research is the detection and analysis of existing trends in the Meta River, Colombia, based on the streamflow records from seven gauging stations in its main course, for the period between June 1983 to July 2019. The Meta River is one of the principal branches of the Orinoco River, and it has a high environmental and economic value for this South American country. The methods employed for the trend detection and quantification were the Mann–Kendall (MK) test, the modified MK (MMK) test, and the Sen’s slope (SS) estimator. Statistically significant trends (at a 95% level of confidence) were detected in more than 30% of the 105 evaluated datasets. The results from the MK test indicate the presence of statistically significant downward trends in the upstream stations and upward trends in the downstream stations, with the latter presenting steep positive slopes. The findings of this study are valuable assets for water resources management and sustainable planning in the Meta River Basin.Arrieta-Castro, MarcoDonado-Rodríguez, AdrianaAcuña Robles, Guillermo Jesús-will be generated-orcid-0000-0002-7233-8161-600Canales, Fausto-will be generated-orcid-0000-0002-6858-1855-600Teegavarapu, Ramesh S. V.Kázmierczak, BartoszengWaterCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Streamflow trendsMann–KendallModified Mann-KendallSens’s slopeMeta RiverAnalysis of streamflow variability and trends in the Meta River, ColombiaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Grizzetti, B.; Lanzanova, D.; Liquete, C.; Reynaud, A.; Cardoso, A.C. Assessing water ecosystem services for water resource management. Environ. Sci. Policy 2016, 61, 194–203. [CrossRef]2. Silva, A.T. Introduction to Nonstationary Analysis and Modeling of Hydrologic Variables. In Fundamentals of Statistical Hydrology; Naghettini, M., Ed.; Springer International Publishing: Cham, Switzerland, 2017; pp. 537–577.3. Arfan, M.; Lund, J.; Hassan, D.; Saleem, M.; Ahmad, A. Assessment of Spatial and Temporal Flow Variability of the Indus River. Resources 2019, 8, 103. [CrossRef]4. Ali, R.; Ismael, A.; Heryansyah, A.; Nawaz, N. Long Term Historic Changes in the Flow of Lesser Zab River, Iraq. Hydrology 2019, 6, 22. [CrossRef]5. Tadese, M.T.; Kumar, L.; Koech, R.; Zemadim, B. Hydro-Climatic Variability: A Characterisation and Trend Study of the Awash River Basin, Ethiopia. Hydrology 2019, 6, 35. [CrossRef]6. Langat, P.; Kumar, L.; Koech, R. Temporal Variability and Trends of Rainfall and Streamflow in Tana River Basin, Kenya. Sustainability 2017, 9, 1963. [CrossRef]7. Yilmaz, A.G.; Perera, B.J.C. Spatiotemporal Trend Analysis of Extreme Rainfall Events in Victoria, Australia. Water Resour. Manag. 2015, 29, 4465–4480. [CrossRef]8. Wdowikowski, M.; Ka ´zmierczak, B.; Ledvinka, O. Maximum daily rainfall analysis at selected meteorological stations in the upper Lusatian Neisse River basin. Meteorol. Hydrol. Water Manag. 2016, 4, 53–63. [CrossRef]9. Pellicciotti, F.; Burlando, P.; Van Vliet, K. Recent trends in precipitation and streamflow in the Aconcagua River Basin, central Chile. In IAHS Assembly; IAHS Publ.: Foz do Iguaçu, Brazil, 2007; pp. 1–22.10. Su, L.; Miao, C.; Kong, D.; Duan, Q.; Lei, X.; Hou, Q.; Li, H. Long-term trends in global river flow and the causal relationships between river flow and ocean signals. J. Hydrol. 2018, 563, 818–833. [CrossRef]11. Feng, J.; Li, D.; Wang, T.; Liu, Q.; Deng, L.; Zhao, L. Acceleration of the Extreme Sea Level Rise Along the Chinese Coast. Earth Space Sci. 2019, 6, 1942–1956. [CrossRef]12. Teegavarapu, R.S.V. Methods for Analysis of Trends and Changes in Hydroclimatological Time-Series. In Trends and Changes in Hydroclimatic Variables; Teegavarapu, R.S.V., Ed.; Elsevier: Amsterdam, The Netherlands, 2019; pp. 1–89.13. ¸Sener, ¸S.; ¸Sener, E.; Davraz, A. Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey). Sci. Total Environ. 2017, 584–585, 131–144. [CrossRef]14. Ndehedehe, C.E.; Ferreira, V.G. Assessing land water storage dynamics over South America. J. Hydrol. 2020, 580, 124339. [CrossRef]15. Vega-Jácome, F.; Lavado-Casimiro, W.S.; Felipe-Obando, O.G. Assessing hydrological changes in a regulated river system over the last 90 years in Rimac Basin (Peru). Theor. Appl. Climatol. 2018, 132, 347–362. [CrossRef]16. Morán-Tejeda, E.; Bazo, J.; López-Moreno, J.I.; Aguilar, E.; Azorín-Molina, C.; Sanchez-Lorenzo, A.; Martínez, R.; Nieto, J.J.; Mejía, R.; Martín-Hernández, N.; et al. Climate trends and variability in Ecuador (1966–2011). Int. J. Climatol. 2016, 36, 3839–3855. [CrossRef]17. Seoane, R.; López, P. Assessing the effects of climate change on the hydrological regime of the Limay River basin. GeoJournal 2007, 70, 251–256. [CrossRef]18. Vich, A.I.J.; López, P.M.; Schumacher, M.C. 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