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Theoretical and Applied Climatology

, Volume 129, Issue 1–2, pp 33–45 | Cite as

Streamflow trend analysis by considering autocorrelation structure, long-term persistence, and Hurst coefficient in a semi-arid region of Iran

  • Reza ZamaniEmail author
  • Rasoul Mirabbasi
  • Sajjad Abdollahi
  • Deepak Jhajharia
Original Paper

Abstract

Due to the substantial decrease of water resources as well as the increase in demand and climate change phenomenon, analyzing the trend of hydrological parameters is of paramount importance. In the present study, investigations were carried out to identify the trends in streamflow at 20 hydrometric stations and 11 rainfall gauging stations located in Karkheh River Basin (KRB), Iran, in monthly, seasonal, and annual time scales during the last 38 years from 1974 to 2011. This study has been conducted using two versions of Mann–Kendall tests, including (i) Mann–Kendall test by considering all the significant autocorrelation structure (MK3) and (ii) Mann–Kendall test by considering LTP and Hurst coefficient (MK4). The results indicate that the KRB streamflow trend (using both test versions) has decreased in all three time scales. There is a significant decreasing trend in 78 and 73 % of the monthly cases using the MK3 and MK4 tests, respectively, while these percentages changed to 80 and 70 % on seasonal and annual time scales, respectively. Investigation of the trend line slope using Theil–Sen’s estimator showed a negative trend in all three time scales. The use of MK4 test instead of the MK3 test has caused a decrease in the significance level of Mann–Kendall Z-statistic values. The results of the precipitation trends indicate both increasing and decreasing trends. Also, the correlation between the area average streamflow and precipitation shows a strong correlation in annual time scale in the KRB.

Keywords

Streamflow Kendall Test Precipitation Trend Significant Autocorrelation Hydrometric Station 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • Reza Zamani
    • 1
    Email author
  • Rasoul Mirabbasi
    • 2
  • Sajjad Abdollahi
    • 1
  • Deepak Jhajharia
    • 3
  1. 1.Department of Hydrology and Water Resources, Faculty of Water Sciences EngineeringShahid Chamran University of AhvazAhvazIran
  2. 2.Department of Water EngineeringShahrekord UniversityShahrekordIran
  3. 3.Department of Agricultural EngineeringNorth Eastern Regional Institute of Science and TechnologyNirjuliIndia

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