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Stochastic Prediction of Drought Class Transitions

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  • Ana Paulo
  • Luis Pereira

Abstract

This paper aims at the stochastic characterization of droughts applying Markov chains modeling to drought class transitions derived from SPI time series. Several sites in Southern Portugal having updated data on precipitation available were considered. The drought class probabilities, the expected residence time in each class of severity, the expected time for the transition between drought classes and the drought severity class predictions 1, 2, or 3 months ahead have been obtained. Those predictions are then compared with observed drought classes for the recent drought periods of 2003–2006. In addition, the estimation of the cumulated precipitation deficits, amount of monthly precipitation needed to decrease drought severity, and foreseen SPI values depending on different precipitation scenarios are also presented as complementing the prediction of drought class transitions. Copyright Springer Science+Business Media B.V. 2008

Suggested Citation

  • Ana Paulo & Luis Pereira, 2008. "Stochastic Prediction of Drought Class Transitions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(9), pages 1277-1296, September.
    • Handle: RePEc:spr:waterr:v:22:y:2008:i:9:p:1277-1296
    DOI: 10.1007/s11269-007-9225-5
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    1. 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.
    2. B. Bonaccorso & I. Bordi & A. Cancelliere & G. Rossi & A. Sutera, 2003. "Spatial Variability of Drought: An Analysis of the SPI in Sicily," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 17(4), pages 273-296, August.
    3. Frank Bickenbach & Eckhardt Bode, 2003. "Evaluating the Markov Property in Studies of Economic Convergence," International Regional Science Review, , vol. 26(3), pages 363-392, July.
    4. Paulo, A.A. & Ferreira, E. & Coelho, C. & Pereira, L.S., 2005. "Drought class transition analysis through Markov and Loglinear models, an approach to early warning," Agricultural Water Management, Elsevier, vol. 77(1-3), pages 59-81, August.
    5. Sergio Vicente-Serrano, 2006. "Differences in Spatial Patterns of Drought on Different Time Scales: An Analysis of the Iberian Peninsula," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(1), pages 37-60, February.
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    3. Jianzhu Li & Shuhan Zhou & Rong Hu, 2016. "Hydrological Drought Class Transition Using SPI and SRI Time Series by Loglinear Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 669-684, January.
    4. Salim Djerbouai & Doudja Souag-Gamane, 2016. "Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Stochastic Models: Case of the Algerois Basin in North Algeria," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2445-2464, May.
    5. Zeyad Tarawneh & Elgaali Elgaali & Moshrik Hamdi, 2009. "Bi-site Analysis of Meteorological Drought Duration: Theoretical Modeling and Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(14), pages 3005-3018, November.
    6. Jianzhu Li & Shuhan Zhou & Rong Hu, 2016. "Hydrological Drought Class Transition Using SPI and SRI Time Series by Loglinear Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 669-684, January.
    7. Ana Paulo & Diogo Martins & Luís Santos Pereira, 2016. "Influence of Precipitation Changes on the SPI and Related Drought Severity. An Analysis Using Long-Term Data Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(15), pages 5737-5757, December.
    8. 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.
    9. Tayeb Raziei & Isabella Bordi & Luis Pereira, 2011. "An Application of GPCC and NCEP/NCAR Datasets for Drought Variability Analysis in Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(4), pages 1075-1086, March.
    10. Iraj Emadodin & Daniel Ernesto Flores Corral & Thorsten Reinsch & Christof Kluß & Friedhelm Taube, 2021. "Climate Change Effects on Temperate Grassland and Its Implication for Forage Production: A Case Study from Northern Germany," Agriculture, MDPI, vol. 11(3), pages 1-17, March.
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    12. Hsin-Fu Yeh & Hsin-Li Hsu, 2019. "Using the Markov Chain to Analyze Precipitation and Groundwater Drought Characteristics and Linkage with Atmospheric Circulation," Sustainability, MDPI, vol. 11(6), pages 1-18, March.
    13. Fhumulani Mathivha & Caston Sigauke & Hector Chikoore & John Odiyo, 2020. "Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models," Sustainability, MDPI, vol. 12(10), pages 1-20, May.

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