Extreme streamflow time series analysis: trends, record length, and persistence

References

• Alves BCC, Souza Filho F, Silveira C. 2013. Análise de tendências e padrões de variação das séries históricas de vazões do operador nacional do sistema (ons). Braz J Water Resour. 18(4):19–34. DOI:10.21168/rbrh.v18n4.p19-34.
   Google Scholar
• Back AJ. 2001. Aplicação de análise estatística para identificação de tendências climáticas. Pesqui agropecu bras. 36(5):717–726. DOI: 10.1590/S0100-204X2001000500001.
   Google Scholar
• Bartiko D, Chaffe PLB, Bonumá NB. 2017. Nonstationarity in maximum annual daily streamflow series from southern Brazil. Braz J Water Resour. 22(48). DOI:https://doi.org/10.1590/2318-0331.0217170054.
   Google Scholar
• Bartiko D, Oliveira DY, Bonumá NB, Chaffe PLB. 2019. Spatial and seasonal patterns of flood change across Brazil. Hydrol Sci J. 64(9):1071–1079. DOI:https://doi.org/10.1080/02626667.2019.161-9081.
   Web of Science ®Google Scholar
• Bellabas SC, Benmamar S, Dehni A. 2020. Study and analysis of the streamflow decline in North Algeria. J Appl Water Eng Res. 8:1–25. DOI:10.1080/23249676.2020.1831974.
   Google Scholar
• Chen Y, Guan Y, Shao G, Zhang D. 2016. Investigating trends in streamflow and precipitation in Huangfuchuan basin with wavelet analysis and the Mann-Kendall test. Water. 8(77):32. DOI:10.3390/w8030077.
   Google Scholar
• Coen MC, Andrews E, Bigi A, Romanens G, Martucci G, Vuilleumier L. 2020. Effects of the prewhitening method, the time granularity and the time segmentation on the Mann-Kendall trend detection and the associated Sen's slope. Atmos Meas Tech Discuss. 1–28. DOI: https://doi.org/10.5194/amt-2020-178.
   Google Scholar
• De Oliveira MMF, De Oliveira JLF, Fernandes PJF, Ebecken NFF. 2020. Análise de Tendências Anuais e Sazonais de Extremos da Temperatura da Superfície do Mar Próximo à Costa da América do Sul no Período de 1979 a 2018. Rev Bras de Geogr Fís. 13(6):2531. DOI: 10.26848/rbgf.v13.6.p2531-2552.
   Google Scholar
• De Souza AS, Amorim RS, Reis DS. 2020. Influência da correlação temporal e da multiplicidade de testes na detecção de tendências de índices de chuva no território brasileiro. Rev Bras de Climatol. 26:107–129. DOI: https://doi.org/10.5380/abclima.v26i0.68124.
   Google Scholar
• Detzel DHM. 2015. Modelagem de séries hidrológicas: uma abordagem de múltiplas escalas temporais. Curitiba (PR): Universidade Federal do Paraná.
   Google Scholar
• Dinpashoh Y, Mirabbasi R, Jhajharia D, Zare Abianeh H, Mostafaeipour A. 2014. Effect of short term and long-term persistence on Identification of temporal trends. J Hydrol Eng. 19(3):617–625. DOI: https://doi.org/10.1061/(ASCE)HE.1943-5584.0000819.
   Web of Science ®Google Scholar
• Dixon H, Lawler DM, Shamseldin AY, Webster P. 2006. The effect of record length on the analysis of river flow trends in Wales and Central England. Conference: Fifth FRIEND World Conference, Cuba, 2006, International Association of Hydrological Sciences. Volume: In: Demuth, S. et al., editors. Climate Variability and Change – Hydrological Impacts; IAHS; Publ. 308. p. 490–495.
   Google Scholar
• Do HX, Westra S, Leonard M. 2017. A global-scale investigation of trends in annual maximum streamflow. J Hydrol. 552:28–43. DOI: 10.1016/j.jhydrol.2017.06.015.
   Web of Science ®Google Scholar
• Dos Santos CA, De Lima AMM, Farias MHCS, Aires URV, De Oliveira Serrão EA. 2016. Análise estatística da não estacionariedade de séries temporais de vazão máxima anual diária na bacia hidrográfica do rio pardo. HOLOS. 7:179–193. DOI: 10.15628/holos.2016.4892.
   Google Scholar
• Dudley RW, Hirsch RM, Archfield SA, Blum AG, Renard B. 2020. Low streamflow trends at human-impacted and reference basins in the United States. J Hydrol. 580:124254. DOI: 10.1016/j.jhydrol.2019.124254.
   Web of Science ®Google Scholar
• Escanciano JC, Lobato IN. 2009. An automatic Portmanteau test for serial correlation. J Econom. 151(2):140–149. DOI: 10.1016/j.jeconom.2009.03.001.
   Web of Science ®Google Scholar
• François B, Schlef KE, Wi S, Brown CM. 2019. Design considerations for riverine floods in a changing climate – a review.J Hydrol. 574:557–573. DOI: 10.1016/j.jhydrol.2019.04.068.
   Web of Science ®Google Scholar
• Güçlü YS. 2020. Improved visualization for trend analysis by comparing with classical Mann-Kendall test and ITA.J Hydrol. 584:124674. DOI: 10.1016/j.jhydrol.2020.124674.
   Web of Science ®Google Scholar
• Gudmundsson L, Leonard M, Do HX, Westra S, Seneviratne SI. 2019. Observed trends in global indicators of mean and extreme streamflow. Geophys Res Lett. 46(2):756–766. DOI: 10.1029/2018gl079725.
   Web of Science ®Google Scholar
• Hamed KH. 2008. Trend detection in hydrologic data: the Mann–Kendall trend test under the scaling hypothesis. J Hydrol. 349(3–4):350–363. DOI: 10.1016/j.jhydrol.2007.11.009.
   Web of Science ®Google Scholar
• Harrison TD, Gilmour G, McNeill MT, Armour N, McIlroy L. 2020. Survey of imposex in Nucella lapillus as an indicator of tributyltin pollution in northern Irish coastal waters, 2004 to 2017. Mar Pollut Bull. 159:111474. DOI: 10.1016/j.marpolbul.2020.111474.
   PubMed Web of Science ®Google Scholar
• Hidalgo JSG, Brunetti M, Jiménez-Castañeda A, Salinas-Solé C, Peña-Ângulo D. 2017. The effect of length and starting year on trend analyses of temperatures in Spanish mainland (1951–2010). A general approach (I). 19th EGU General Assembly, EGU2017, Proceedings from the Conference held Apr 23–28, Vienna, Austria.
   Google Scholar
• Isensee LJ, Pinheiro A, Detzel DHM. 2021. Dam hydrological risk and the design flood under non-stationary conditions. Water Resour Manag. DOI: 10.1007/s11269-021-02798-3.
   Google Scholar
• Jaiswal RK, Lohani AK, Tiwari HL. 2015. Statistical analysis for change detection and trend assessment in climatological parameters. Environ Process. 2:729–749. DOI: 10.1007/s40710-015-0105-3.
   Google Scholar
• Junqueira HS, de Almeida LMF, de SOUZA TS, dos SANTOS PN. 2020. Análise da Variação Sazonal e de Tendências na Precipitação Pluviométrica no Município de Juazeiro-BA. Rev Bras Geogr Fís. 13(6):2641. DOI: 10.26848/rbgf.v13.6.p2641-2649.
   Google Scholar
• Khaliq M, Ouarda T, Gachon P, Sushama L, St-Hilaire A. 2009. Identification of hydrological trends in the presence of serial and cross correlations: a review of selected methods and their application to annual flow regimes of Canadian rivers. J Hydrol. 368(1–4):117–130. DOI: 10.1016/j.jhydrol.2009.01.035.
   Web of Science ®Google Scholar
• Koscielny-Bunde E, Kantelhardt JW, Braun P, Bunde A, Havlin S. 2006. Long-term persistence and multifractality of river runoff records: detrended fluctuation studies. J Hydrol. 322(1–4):120–137. DOI: 10.1016/j.jhydrol.2005.03.004.
   Web of Science ®Google Scholar
• Koutsoyiannis D, Montanari A. 2007. Statistical analysis of hydroclimatic time series: uncertainty and insights. Water Resour Res. 43(5):2007. DOI: 10.1029/2006wr005592.
   Web of Science ®Google Scholar
• Kumar S, Merwade V, Kam J, Thurner K. 2009. Streamflow trends in Indiana: effects of long term persistence, precipitation and subsurface drains. J Hydrol. 374(1–2):171–183. DOI: 10.1016/j.jhydrol.2009.06.012.
   Web of Science ®Google Scholar
• Kundzewicz Z, Robson A. 2000. Detecting Trend and Other Changes in Hydrological Data. Geneva: WMO.
   Google Scholar
• Lira FA, Cardoso AO. 2018. Estudo de tendência de vazões de rios das principais bacias hidrográficas brasileiras. Rev Bras de Ciênc Ambient. 48:21–37. DOI: https://doi.org/10.5327/z2176-947820-180273.
   Google Scholar
• Machekposhti K, Sedghi H, Telvari A, Babazadeh H. 2019. The Karkheh river streamflow forecast based on the modelling of time series. Adv Res Civil Eng. 1(3):49–57. DOI: 10.30469/ARCE.2019.89282.
   Google Scholar
• Mahdi E, Mcleod AI. 2020. Package portes. Cran.R-project.
   Google Scholar
• Montanari A, Rosso R, Taqqu MS. 2000. A seasonal fractional ARIMA model applied to the Nile River monthly flows at Aswan. Water Resour Res. 36(5):1249–1259. DOI: 10.1029/2000WR900012.
   Web of Science ®Google Scholar
• Moreira J, Aquino APV, Mesquita AA, Muniz MA, Serrano ROP. 2019. Stationarity in annual daily maximum streamflow series in the Hydrographic Basin of the Upper Juruá river, western Amazon. Rev Bras de Geogr Fís. 12(2):705–713. DOI: 10.26848/rbgf.v12.2.p705-713.
   Google Scholar
• Naguettini M, Pinto EJA. 2007. Hidrologia Estatística. Belo Horizonte: CPRM.
   Google Scholar
• Nashwan MS, Ismail T, Ahmed K. 2019. Non-stationary analysis of extreme rainfall in peninsular Malaysia. J Sustain Sci Manag. 14:17–34.
   Google Scholar
• Neves GL, Beruski GC, das Virgens Filho JS, Mauad FF. 2020. Variability and trend of air temperature and rainfall at Ribeirão do Lobo hydrographic basin, Brazil. Rev Bras de Geogr Fís. 13(1):035. DOI: https://doi.org/10.26848/rbgf.v13.1.p035-048.
   Google Scholar
• O'Brien NL. 2018. Approaches for nonstationary flood frequency and trend analyses for hydrometric data. [M.S. thesis]. Waterloo: University of Waterloo.
   Google Scholar
• OCHA. 2019. Natural disasters in Latin America and the Caribbean: 2000–2019. New York: United Nations Office for the Coordination of Humanitarian Affairs.
   Google Scholar
• Önöz B, Bayazit M. 2012. Block bootstrap for Mann-Kendall trend test of serially dependent data. Hydrol Process. 26(23):3552–3560. DOI: 10.1002/hyp.8438.
   Web of Science ®Google Scholar
• Patakamuri SK, O'Brien N. 2020. Package modifiedmk. Cran.R-project.
   Google Scholar
• Penereiro JC, Badinger A, Maccheri NA, Meschiatti MC. 2018. Distribuições de tendências sazonais de temperatura média e precipitação nos biomas brasileiros. Rev Bras de Meteorol. 33(1):97–113. DOI: 10.1590/0102-7786331012.
   Google Scholar
• Petrow T, Merz B. 2009. Trends in flood magnitude, frequency and seasonality in Germany in the period 1951–2002.J Hydrol. 371(1-4):129–141. DOI: https://doi.org/10.1016/j.jhydrol.2009.03.024.
   Web of Science ®Google Scholar
• Pinheiro A, Graciano RLG, Severo DL. 2013. Tendência das séries temporais de precipitação da região sul do brasil. Rev Bras Meteorol. 28(3):281–290. DOI: 10.1590/s0102-77862-013000300005.
   Google Scholar
• Rahman S, Bowling L. 2019. Streamflow impacts of management and environmental change in the upper Wabash river basin.J Hydrol Eng. 24(3):05018034. DOI: 10.1061/(asce)he.1943-5584.0001750.
   Web of Science ®Google Scholar
• Razmi A, Golian S, Zahmatkesh Z. 2017. Non-stationary frequency analysis of extreme water level: application of annual maximum series and peak-over threshold approaches. Water Resour Manag. 31(7):2065–2083. DOI: 10.1007/s11269-017-1619-4.
   Web of Science ®Google Scholar
• Rosin C, Amorim R, Morais T. 2015. Análise de tendências hidrológicas na bacia do rio das mortes. Braz J Water Resour. 20(4):991–998. DOI: 10.21168/rbrh.v20n4.p991-998.
   Google Scholar
• Sagarika S, Kalra A, Ahmad S. 2014. Evaluating the effect of persistence on long-term trends and analyzing step changes in streamflows of the continental United States. J Hydrol. 517:36–53. DOI: 10.1016/j.jhydrol.2014.05.002.
   Web of Science ®Google Scholar
• Sanches F, Verdum R, Fisch G. 2014. Long-term trend of daily rainfall in southwest of Rio Grande Do Sul: extreme events and the sandization. Re Bras Geogr Fís. 7(6):1100–1109. DOI: 10.5935/1984-2295.20140012.
   Google Scholar
• Silva FDDS, Costa RL, Antônio MAV, Afonso EO, dos Santos DM, Mateus NPA, Antônio JF. 2018. Tendências observadas da evapotranspiração potencial no estado de Alagoas (1961-2016). Re Bras Geogr Fís. 11(1):028–043. DOI: 10.26848/rbgf.v11.1.p028-043.
   Google Scholar
• SNIRH. 2020. HidroWeb. Brasília, DF: Sistema Nacional de Informações sobre Recursos Hídricos.
   Google Scholar
• Stenico A, Beduschi C, Moraes J, Groppo J, Salemi L, Trevisan R. 2009. Análise do efeito da operação das barragens do sistema Cantareira no regime hidrológico do rio Piracicaba. Braz J Water Resour. 14(1):41–51. DOI: 10.21168/rbrh.v14n1.p41-51.
   Google Scholar
• Svensson C, Kundzewicz ZW, Maurer T. 2005. Trend detection in river flow series: 2. Flood and low-flow index series / Détection de tendance dans des séries de débit fluvial: 2. Séries d'indices de crue et d'étiage. Hydrol Sci J. 50(5):824. DOI: 10.1623/hysj.2005.50.5.811.
   Google Scholar
• Tan X, Gan TY. 2015. Nonstationary analysis of annual maximum streamflow of Canada. J Clim. 28(5):1788–1805. DOI: 10.1175/jcli-d-14-00538.1.
   Web of Science ®Google Scholar
• UNISDR. 2009. Terminology – disaster risk reduction. Geneva: The United Nations Office for Disaster Risk Reduction.
   Google Scholar
• Villarini G, Serinaldi F, Smith JA, Krajewski WF. 2009. On the stationarity of annual flood peaks in the continental United States during the 20th century. Water Resour Res. 45(8). DOI: 10.1029/2008wr007645.
   Google Scholar
• Von Storch H. 1999. Misuses of statistical analysis in climate research. In: Analysis of climate variability. New York (NY) : Springer Berlin Heidelberg; p. 11–26. DOI: 10.1007/978-3-662-03744-7_2.
   Google Scholar
• Wang F, Shao W, Yu H, Kan G, He X, Zhang D, Ren M, Wang G. 2020. Re-evaluation of the power of the Mann-Kendall test for detecting monotonic trends in Hydrometeorological time series. Front Earth Sci. 8:14. DOI: 10.3389/feart.2020.00014.
   Web of Science ®Google Scholar
• Wang W, Chen Y, Becker S, Liu B. 2015. Linear trend detection in serially dependent Hydrometeorological data based on a variance correction Spearman Rho method. Water. 7:7045–7065. DOI: 10.3390/w7126673.
   Web of Science ®Google Scholar
• Weatherhead EC, Reinsel GC, Tiao GC, Meng XL, Choi D, Cheang WK, Keller T, DeLuisi J, Wuebbles DJ, etal ., 1998. Factors affecting the detection of trends: statistical considerations and applications to environmental data. J Geophys Res. 103(14):149–161. DOI: 10.1029/98JD00995.
   Google Scholar
• Weron R. 2002. Estimating long-range dependence: finite sample properties and confidence intervals. Phy A: Stat Mech Appl. 312:285–299.
   Web of Science ®Google Scholar
• Weron R, Przybyłowicz B. 2000. Hurst analysis of electricity price dynamics. Phys A: Stat Mech Appl. 283(3-4):462–468. DOI: 10.1016/s0378-4371(00)00231-4.
   Google Scholar
• Yue S, Pilon P, Phinney B, Cavadias G. 2002. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol Process. 16(9):1807–1829. DOI: 10.1002/hyp.1095.
   Web of Science ®Google Scholar
• Yue S, Wang C. 2004. The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resour Manag. 18(3):201218. DOI: 10.1023/b:warm.0000043140.61082.60.
   Web of Science ®Google Scholar
• Zamani R, Mirabbasi R, Abdollahi S, Jhajharia D. 2017. Streamflow trend analysis by considering autocorrelation structure, long-term persistence, and hurst coefficient in a semi-arid region of Iran. Theor Appl Climatol. 129:33–45. DOI: 10.1007/s00704-016-1747-4.
   Web of Science ®Google Scholar
• Zhang Q, Gu X, Singh VP, Xiao M, Xu CY. 2014. Stationarity of annual flood peaks during 1951–2010 in the pearl river basin, China. J Hydrol. 519:32633274. DOI: 10.1016/j.jhydrol.2014.10.028.
   Web of Science ®Google Scholar