Introduction and application of non-stationary standardized precipitation index considering probability distribution function and return period
The widely used meteorological drought index, the Standardized Precipitation Index (SPI), basically assumes stationarity, but recent changes in the climate have led to a need to review this hypothesis. In this study, a new non-stationary SPI that considers not only the modified probability distribution parameter but also the return period under the non-stationary process was proposed. The results were evaluated for two severe drought cases during the last 10 years in South Korea. As a result, SPIs considered that the non-stationary hypothesis underestimated the drought severity than the stationary SPI despite that these past two droughts were recognized as significantly severe droughts. It may be caused by that the variances of summer and autumn precipitation become larger over time then it can make the probability distribution wider than before. This implies that drought expressions by statistical index such as SPI can be distorted by stationary assumption and cautious approach is needed when deciding drought level considering climate changes.
This work is supported by the “Research and Development for KMA Weather, Climate, and Earth system Services” of National Institute of Meteorological Sciences (NIMS).
- Bedient PB, Huber WC (2001) Hydrology and floodplain analysis, 3rd edn. Prentice-Hall Publishing Co., Upper Saddle RiverGoogle Scholar
- Caroni C, Panagoulia D (2016) Non-stationary modeling of extreme temperatures in a mountainous area of Greece. Revstat Stat J 14(2):217–228Google Scholar
- Li JZ, Wang YX, Li SF, Hu R (2015) A nonstationary standardized precipitation index incorporating climate indices as covariates. J Geophys Res: Atmos 120:82–95Google Scholar
- McKee, T. B., N.J. Doesken, and J. Kleist, (1993). The relationship of drought frequency and duration to time scales. 8th Conf. On applied climatology. Anaheim, CA, American Meteorological Society, pp. 179–184Google Scholar
- Rao AR, Hamed KH (2000) Flood frequency analysis. CRC Press, Boca RatonGoogle Scholar
- Stedinger JR, Vogel RM, Foufoula-Georgiou E (1993) Chapter 18, frequency analysis of extreme events, handbook of hydrology, edited by Maidment, D.R. McGraw-Hill, New YorkGoogle Scholar
- Sung JH, Kim Y-O, Jeon JJ (2017b) Application of distribution-free nonstationary regional frequency analysis based on L-moments. Theor Appl Climatol. https://doi.org/10.1007/s00704-017-2249-8
- Vicente-Serrano SM, Garcia-Herrera R, Barriopedro D, Azorin-Molina C, Lopez-Moreno JI, Martin-Hernandez N, Tomas-Burguera M, Gimemo L, Nieto R (2016) The westerly index as complementary indicator of the North Atlantic oscillation in explaining drought variability across Europe. Clim Dyn 47:845–863. https://doi.org/10.1007/s00382-015-2875-8 CrossRefGoogle Scholar