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Fuzzy standardized precipitation index (FSPI) for drought early warning procedure

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Abstract

There are several standardized meteorological, hydrological, agricultural, and environmental indices for drought classification into a set of dry categories all of which are derived from the basic concept of standardized precipitation index (SPI). Almost all these indices are based on crisp (bivalent) logic where boundary limits between neighboring categories are numbers without transboundary inclusiveness. The SPI procedure depends on the standard normal (Gaussian) probability distribution function (PDF) with zero mean and standard deviation equal to one. This paper proposed fuzzification of the SPI limits among categories for the inclusion of more than one category with different membership degrees. This method is referred to as the fuzzy SPI (FSPI) procedure, which provides drought tracing possibility and categorization. At the end for a numerical value, one can defuzzify the fuzzy result through defuzzification methods. FSPI provides preliminary warning system in terms of two or more fuzzy rule propositions and categories. Thus, one can know the logical alternatives of the drought behavior of a given hydro-meteorology variable. The application of the FSPI is presented for New Jersey State wise and Istanbul annual precipitation records.

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Data availability

Precipitation data were obtained from the General Directorate of Meteorology (TSMS). Data can be provided upon request from the corresponding author.

References

  • Bas E, Grosan C, Egrioglu E, Yolcu U (2018) High order fuzzy time series method based on pi-sigma neural network. Eng Appl Artif Intell 72:350–356

    Article  Google Scholar 

  • Cai Q, Zhang D, Zheng W, Leung SCH (2015) A new fuzzy time series forecasting model combinedwith ant colony optimization and auto-regression. Knowledge-Based Syst 74:61–68

    Article  Google Scholar 

  • Cardona OD, van Aalst MK, Birkmann J, Fordham M, McGregor G, Perez R, Pulwarty RS, Schipper ELF, Singh BT (2012) Determinants of risk: exposure and vulnerability. In Managing the risks of extreme events and disasters to advance climate change adaptation. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK et al (eds) A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK; New York, NY, USA, pp 65–108

    Google Scholar 

  • Chaudhary MT, Piracha A (2021) Natural disasters—origins, impacts, management. Encyclopedia 1:1101–1131. https://doi.org/10.3390/encyclopedia1040084

    Article  Google Scholar 

  • Chen SM (1996) Forecasting enrollment based on fuzzy time series. Fuzzy Sets Syst 81:311–319

    Article  Google Scholar 

  • Dubrovsky M, Svoboda MD, Trnka M, Hayes MJ, Wilhite DA, Zalud Z, Hlavinka P (2009) Application of relative drought indices in assessing climate-change impacts on drought conditions in Czechia. Theor Appl Climatol 96:155–171. https://doi.org/10.1007/s00704-008-0020-x

    Article  Google Scholar 

  • Habibi B, Meddi M, Paul JJF, Remaoun M, Henny AJ (2018) Characterization and prediction of meteorological drought using stochastic models in the semi-arid Chéliff–Zahrez basin (Algeria). J Hydrol Reg Stud 16:15–31. https://doi.org/10.1016/j.ejrh.2018.02.005

    Article  Google Scholar 

  • Hughes JP, Guttorp P (1994) A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water Resour Res 30(5):1535–1546

    Article  Google Scholar 

  • Ji L, Peters AJ (2003) Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sens Environ 87:85–98

    Article  Google Scholar 

  • Madadgar S, Hamid M (2014) Spatio-temporal drought forecasting within Bayesian networks. J Hydrol 512(6):134–146. https://doi.org/10.1016/j.jhydrol.2014.02.039

    Article  Google Scholar 

  • Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 7(1):1–13

    Article  Google Scholar 

  • McKee TB, Doesken NJ, Kliest J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology. Amer. Meteor. Soc., Anaheim, CA, pp 179–184

    Google Scholar 

  • Mishra AK, Desai VR (2005) Drought forecasting using stochastic models. Stoch Env Res Risk A 19(5):326–339

    Article  Google Scholar 

  • Palmer WC (1965) Meteorological Drought, Office of Climatology. US Weather Bureau, Research Paper No. 45, Washington, DC, p 58. http://www.ncdc.noaa.gov/temp-and-precip/drought/docs/palmer.pdf

  • Ross JT (1995) Fuzzy logic with engineering applications. McGraw-Hill, Inc., New York, p 593

    Google Scholar 

  • Shukla S, Wood AW (2008) Use of a standardized runoff index for characterizing hydrologic drought. Geophys Res Lett 35:L02405. https://doi.org/10.1029/2007GL032487

    Article  Google Scholar 

  • Smith K (2013) Environmental hazards: assessing risk and reducing disaster, 6th edn. Routledge, Oxford, UK, p 470

    Book  Google Scholar 

  • Slakter MJ (1965) A comparison of the Pearson chi-square and Kolmogorov goodness-of-fit tests with respect to validity. J Am Stat Assoc 60(311):854–858. https://doi.org/10.2307/2283251

    Article  Google Scholar 

  • Van Huijgevoort MHJ, Van Lanen HAJ, Teuling AJ, Uijlenhoet R (2014) Identification of changes in hydrological drought characteristics from a multi-GCM driven ensemble constrained with observed discharge. J Hydrol 512:421–434. https://doi.org/10.1016/j.jhydrol.2014.02.060

    Article  Google Scholar 

  • Vicente-Serrano SM, Beguería S, A López-Moreno JI. (2010) Multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23:1696–1718

    Article  Google Scholar 

  • Vicente-Serrano SM, Beguería S, Lorenzo-Lacruz J, Camarero JJ, López-Moreno JI, Azorin-Molina C, Revuelto J, Morán-Tejeda E, Sanchez-Lorenzo (2012) Performance of drought indices for ecological, agricultural, and hydrological applications. Earth Interact 16:1–27

    Article  Google Scholar 

  • Weibull W (1939) A statistical theory of strength of materials. Ing Vetensk Akad Handl 151:1–45

    Google Scholar 

  • White GF, Kates RW, Burton I (2001) Knowing better and losing even more: the use of knowledge in hazards management. Environ Hazards 3:81–92

    Google Scholar 

  • WMO, World Meteorological Organization (2006) Drought monitoring and early warning: concepts, progress and future challenges. WMO-No. 1006, Geneva, Switzerland, p 24

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  Google Scholar 

  • Zadeh, L.A., (1968). Fuzzy algorithms. Information and Control. San Diego, California: Academic Press. 12(2): 94–102. https://doi.org/10.1016/S0019-9958(68)90211-8

    Book  Google Scholar 

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Acknowledgements

The author thanks the Turkish State Meteorological Service (TSMS) for the data provided.

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Correspondence to Zekâi Şen.

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Şen, Z. Fuzzy standardized precipitation index (FSPI) for drought early warning procedure. Theor Appl Climatol 155, 1281–1287 (2024). https://doi.org/10.1007/s00704-023-04691-y

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