The performance of SPI and PNPI in analyzing the spatial and temporal trend of dry and wet periods over Iran

Babak Amirataee () and Majid Montaseri
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Babak Amirataee: Urmia University
Majid Montaseri: Urmia University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2017, vol. 86, issue 1, No 4, 89-106

Abstract: Abstract Drought monitoring is carried out using various drought indices, including SPI, to generate time series of dry and wet periods. Furthermore, the dispersion of dry and wet periods was embossed with different intensities (high, medium, and low) over the data record years. Although these results were very necessary for planning and predicting future droughts, it appeared that the application of any trend over dry and wet periods could provide more accurate and unbiased or safer predictions in terms of analysis process. Generally, most of the researchers believed that the results of a drought trend analysis have been influenced by short-term persistence or significant autocorrelation with different lags on drought event time series and the mentioned impact should be preferably removed. Accordingly, drought monitoring was accomplished using SPI and PNPI drought indices to extract time series of dry and wet periods in terms of 50-year (1965–2014) annual rainfall data of 40 synoptic stations over Iran. Having used the basic and modified Mann–Kendall nonparametric tests, it was attempted to analyze the trend of dry and wet periods extracted from mentioned indices. The results represent the relative advantage of using the modified Mann–Kendall test in drought trend analysis. Furthermore, it was shown that the trend of dry and wet periods was negative in the majority of selected stations and that this trend was significant at 95% confidence level in northwest of Iran. Also, the results indicated the similar performance of SPI and PNPI indices in trend analysis of dry and wet periods.

Keywords: Autocorrelation coefficient; Drought; Iran; PNPI; SPI; Trend analysis (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11069-016-2675-4

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