Title: Investigation of long-memory properties in streamflow time series in Gamasiab River, Iran

Authors: Reza Hadizadeh; Saeid Eslamian; Rahim Chinipardaz

Addresses: Department of System Management and Productivity, Collage of Industrial Engineering, Islamic Azad University – Tehran South Branch, Tehran, 82883460, Iran ' Department of Water Engineering, Collage of Agriculture, Isfahan University of Technology, Isfahan, 8415683111, Iran ' Department of Statistics, Collage of Mathematical Science, Shahid Chamran University, Ahvaz, 3330040, Iran

Abstract: In this article, we have discussed about long-memory of processes of time series at Polchehr, Polkohne and Heydarabad hydrometric stations of Gamasiab River at Kermanshah by using daily, monthly and seasonal (1/4-year) discharge average data through heuristic, semi-parametric and parametric methods. Also, we have discussed about existence of long-memory and comparing its intensity at three timescales. If we were able to model xt series to (1 - L)dxt = εt in which εt ∼ N(0, σ2) is white noise and also 0 ≤ d ≤ 1, then series has long-memory property. If 0 ≤ d ≤ 0.5 so series variance is finite and series is generally stationary. If 0.5 ≤ d ≤ 1, series variance is infinite and series is non-stationary. The results of each of the three methods show that daily steamflow processes exhibit strong long-memory. By increasing timescale of processes, intensity of long-memory decreases. Streamflow processes in monthly timescale show less evidence of the existence of long-memory however in seasonal streamflow time series, there is no long-memory.

Keywords: long-memory properties; streamflow time series; Iran; classic R/S analysis; aggregated variance method; detrended fluctuation analysis; DFA; MRS test; GPH test.

DOI: 10.1504/IJHST.2013.060335

International Journal of Hydrology Science and Technology, 2013 Vol.3 No.4, pp.319 - 350

Published online: 12 Jul 2014 *

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