Identification of Trend in Reference Evapotranspiration Series with Serial Dependence in Iran

Hossein Tabari (), Jaefar Nikbakht and P. Hosseinzadeh Talaee

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2012, vol. 26, issue 8, 2219-2232

Abstract: Monitoring the temporal variations of reference evapotranspiration (ET o ) and quantifying any trends offer valuable information for regional hydrology, agricultural water requirements and water resources management. This study aimed to examine the temporal trends in the Penman–Monteith ET o in the west and southwest of Iran by using the Kendall and Spearman tests after eliminating the influence of significant lag-1 serial correlation from the ET o time series. The magnitudes and starting years of significant ET o trends were determined by the Mann-Kendall rank statistic and the Theil–Sen’s estimator, respectively. For the study period of 1966–2005, a significant positive lag-1 serial correlation coefficient was observed at almost all the stations. The existence of the positive serial correlation in the ET o series increased the possibility of the Kendall and Spearman tests to reject the null hypothesis of no trend while it is true. It was found that the Kendall test was more sensitive than the Spearman test to the existence of the positive serial correlation in the ET o series. After removing the serial correlation effect with pre-whitening method, only three significant increasing ET o trends were obtained at Khorram-Abad, Shahrekord and Zanjan stations at the rates of 0.16, 0.06 and 0.06 mm/day per decade, respectively. The significant increasing ET o trends of Khorram-Abad, Zanjan and Shahrekord stations started in 1997, 1994 and 1998, respectively. The stepwise regression method showed that wind speed was the most dominating variable affecting on the significant changes of ET o . Copyright Springer Science+Business Media B.V. 2012

Keywords: Kendall test; Penman-Monteith model; Pre-whitening; Spearman test; Serial correlation; Trend magnitude (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/s11269-012-0011-7

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