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Drought Monitoring Using the Multivariate Standardized Precipitation Index (MSPI)

Author

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  • Javad Bazrafshan
  • Somayeh Hejabi
  • Jaber Rahimi

Abstract

One major characteristic of the Standardized Precipitation Index (SPI) is its flexibility to be calculated in a variety of time scales and hence being aware of different types of droughts. However, various time scales may result in confusion of the water resources’ researchers, decision makers and users in identifying and specifying drought periods in a certain region. To solve this problem in this article, a multivariate approach has been utilized having the ability to aggregate a variety of the SPI time series into a new time series called the Multivariate Standardized Precipitation Index (MSPI). The MSPI is based on the principal components analysis (PCA) of the SPI time series in a certain location. Having specified the first principal component’s (PC 1 ’s) scores, the MSPI would be simply attained by dividing PC 1 ’s values by the monthly standard deviation. In this article, MSPI’s capability in depicting the variability of the SPI time series (in five ranges of time scales, including 3–6, 6–12, 3–12, 12–24, and 24–48 months) was studied at four weather stations representing the four different climates in Iran from the driest to the wettest climates. The results showed that the PC 1 is able to justify more than 74 % of the variability for the selected sets of the SPI time scales in the studied climates. Also, it became clear that the drought and wet severity classes monitored by MSPIs matched very satisfyingly to those of the five sets of the SPI time scales. Therefore, in cases where the aggregation time scales for calculating the SPI are not previously known, this study recommends the researchers use the MSPI. Copyright Springer Science+Business Media Dordrecht 2014

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  • Javad Bazrafshan & Somayeh Hejabi & Jaber Rahimi, 2014. "Drought Monitoring Using the Multivariate Standardized Precipitation Index (MSPI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1045-1060, March.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:4:p:1045-1060
    DOI: 10.1007/s11269-014-0533-2
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    2. Ana Paulo & Luis Pereira, 2007. "Prediction of SPI Drought Class Transitions Using Markov Chains," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(10), pages 1813-1827, October.
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    4. Jin Huang & Shanlei Sun & Yan Xue & Jinjian Li & Jinchi Zhang, 2014. "Spatial and Temporal Variability of Precipitation and Dryness/Wetness During 1961–2008 in Sichuan Province, West China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(6), pages 1655-1670, April.
    5. Javad Bazrafshan & Mehdi Nadi & Khalil Ghorbani, 2015. "Comparison of Empirical Copula-Based Joint Deficit Index (JDI) and Multivariate Standardized Precipitation Index (MSPI) for Drought Monitoring in Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 2027-2044, April.
    6. Jincai Zhao & Qianqian Liu & Heli Lu & Zheng Wang & Ke Zhang & Pan Wang, 2021. "Future droughts in China using the standardized precipitation evapotranspiration index (SPEI) under multi-spatial scales," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 615-636, October.
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    8. Zahra Fahimirad & Nazanin Shahkarami, 2021. "The Impact of Climate Change on Hydro-Meteorological Droughts Using Copula Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3969-3993, September.
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    13. Ayşegül Kuzucu & Gülay Onuşluel Gül, 2023. "Analysis of Drought Dynamics over Annual Maximum Drought Severity Series Based on Daily Index Definitions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1421-1436, February.
    14. Qiang Zhang & Tianyao Qi & Vijay Singh & Yongqin Chen & Mingzhong Xiao, 2015. "Regional Frequency Analysis of Droughts in China: A Multivariate Perspective," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1767-1787, April.
    15. Alireza Shokoohi & Reza Morovati, 2015. "Basinwide Comparison of RDI and SPI Within an IWRM Framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 2011-2026, April.

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