Skip to main content
SpringerLink
Log in

Factors Influencing Markov Chains Predictability Characteristics, Utilizing SPI, RDI, EDI and SPEI Drought Indices in Different Climatic Zones

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Comparability analyses were carried out to investigate behavioural aspects of effective drought index (EDI), standardized precipitation index (SPI), reconnaissance drought index (RDI) and standardized precipitation evapotranspiration index (SPEI), considering 3-month, 6-month and annual time periods. Investigations included parametric/non-parametric correlation analysis among indices, climatic zone influence, record length impacts and evapotranspiration role (RDI and SPEI) on Markov chains predictability characteristics. Except for the EDI, all indices/cases (all climatic zones) showed significant correlation. In arid/semi-arid climates, the 3-month and 6-month maximum drought severities were detected by the RDI and annual maximum drought severities were detected by the SPEI, emphasizing the evapotranspiration influence. In all climatic zones, the EDI values for wet (dry) periods were higher (lower), compared to other indices. First order dependency was detected for the EDI (all cases) and the SPI (most cases), over entire period (1951–2011) and sub-periods [(1951–1981), (1982–2011)]. The largest number of second order dependency was detected by the SPEI, followed by a relatively large number of such cases by the RDI (3-month time period), for the 61-year data period. This research showed that several factors influence Markov chains predictability characteristics in drought studies, particularly the impact of record length and evapotranspiration (RDI and SPEI) were confirmed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Abramowitz M, Stegun IA (1965) Handbook of mathematical functions. Dover Publications, New York

    Google Scholar 

  • Ahmad MI, Sinclair CD, Werrity A (1988) Log-logistic flood frequency analysis. J Hydrol 98:205–224

    Article  Google Scholar 

  • Akaike H (1974) A new look at statistical model identification. IEEE Trans Autom Control AC 19:716–723

    Article  Google Scholar 

  • Byun HR, Wilhite DA (1996) Daily quantification of drought severity and duration. J Clim 5:1181–1201

    Google Scholar 

  • De Martonne E (1942) Nouvelle carte mondiale de l’indice d’aridité. Ann Géogr 51:242–250

    Article  Google Scholar 

  • Droogers P, Allen RG (2002) Estimating reference evapotranspiration under inaccurate data conditions. Irrig Drain Syst 16:33–45

    Article  Google Scholar 

  • Guttman NB (1999) Accepting the standardized precipitation index: a calculation algorithm. J Am Water Resour Assoc 35:311–322

    Article  Google Scholar 

  • Hargreaves GL, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1:96–99

    Google Scholar 

  • Hosking JRM, Wallis JR (1997) Regional frequency analysis: an approach based L-moments. Cambridge University Press, Cambridge, UK

    Book  Google Scholar 

  • Khalili D, Farnoud T, Jamshidi H, Kamgar-Haghighi AA, Zand-Parsa S (2011) Comparability analyses of the SPI and RDI meteorological drought indices in different climatic zones. Water Resour Manage 25:1737–1757

    Article  Google Scholar 

  • Kim DW, Byun HR, Choi KS (2009) Evaluation, modification, and application of the Effective Drought Index to 200-Year drought climatology of Seoul, Korea. J Hydrol 378:1–12

    Article  Google Scholar 

  • Knight WE (1996) A computer method for calculating Kendall’s Tau with ungrouped data. J Am Stat Assoc 61:436–439

    Article  Google Scholar 

  • McKee, TB, Doesken, NJ, Kleist, J (1993) The relationship of drought frequency and duration to time scales. In: Proc. 8th Conf on Appl Climatol, 17–22 January, Am Meteorol Soc, Mass, 179–184

  • McKee, TB, Doesken, NJ, Kleist, J (1995) Drought monitoring with multiple time scales. Paper Presented at 9th Conference on Applied Climatology. American Meteorological Society, Dallas, Texas

  • Modarres R (2010) Regional dry spell frequency analysis by L-moment and multivariate analysis. Water Resour Manage 24:2365–2380

    Article  Google Scholar 

  • Morid S, Smakhtin VU, Moghadasi M (2006) Comparison of seven meteorological indices for drought monitoring in Iran. Int J Climatol 26:971–985

    Article  Google Scholar 

  • Nalbantis I, Tsakiris G (2009) Assessment of hydrological droughts revisited. Water Resour Manage 23:881–897

    Article  Google Scholar 

  • Palmer WC (1965) Meteorological Drought. Research Paper No. 45. U.S. Department of Commerce Weather Bureau, Washington, DC

    Google Scholar 

  • Paulo AA, Pereira LS (2007) Stochastic prediction of SPI drought class transition. Water Resour Manage 22:1277–1527

    Article  Google Scholar 

  • Raziei T, Saghafian B, Paulo AA, Pereira LS, Bordi I (2009) Spatial patterns and temporal variability of drought in western Iran. Water Resour Manage 23:439–455

    Article  Google Scholar 

  • Raziei T, Bordi I, Pereira LS (2013) Regional drought modes in Iran using the SPI: the effect of time scale and spatial resolution. Water Resour Manage 27:1661–1674

    Article  Google Scholar 

  • Sarhadi A, Heydarizadeh M (2013) Regional frequency analysis and spatial pattern characterization of Dry Spells in Iran. Int J Climatol. doi:10.1002/joc.3726

    Google Scholar 

  • Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464

    Article  Google Scholar 

  • Shahabfar A, Ghulam A, Eitzinger J (2012) Drought monitoring in Iran using the perpendicular drought indices. Int J Appl Earth Obs 18:119–127

    Article  Google Scholar 

  • Singh VP, Guo FXY (1993) Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome. Stoch Hydrol Hydraul 7:163–177

    Article  Google Scholar 

  • Smakhtin VU, Hughes DA (2007) Automated estimation and analyses of meteorological drought characteristics from monthly rainfall data. Environ Modell Softw 22:880–890

    Article  Google Scholar 

  • Tabrizi AA, Khalili D, Kamgar-Haghighi AA, Zand-Parsa S (2010) Utilization of time-based meteorological droughts to investigate occurrence of stramflow droughts. Water Resour Manage 24:4287–4306

    Article  Google Scholar 

  • Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev 38:55–94

    Article  Google Scholar 

  • Tsakiris, G (2004) Meteorological drought assessment. Paper prepared for the needs of the European Research Program MEDROPLAN (Mediterranean Drought Preparedness and Mitigation Planning), Zaragoza, Spain

  • Tsakiris G, Vangelis H (2005) Establishing a drought index incorporating evapotranspiration. Eur Water (9/10):3–11

    Google Scholar 

  • Tsakiris G, Pangalou D, Vangelis H (2007) Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resour Manage 21:821–833

    Article  Google Scholar 

  • Vangelis H, Spiliotis M, Tsakiris G (2011) Drought severity assessment based on bivariate probability analysis. Water Resour Manage 25:357–371

    Article  Google Scholar 

  • Vangelis H, Tigkas D, Tsakiris G (2013) The effect of PET method on reconnaissance drought index (RDI) calculation. J Arid Environ 88:130–140

    Article  Google Scholar 

  • Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multi-scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index–SPEI. J Climate 23:1696–1718

    Article  Google Scholar 

  • Wilks, DS (2006) Statistical methods in the atmospheric science, 2nd edn. AP, NY

  • Willmott CJ (1981) On the validation of models. Phys Geogr 2:184–194

    Google Scholar 

  • Yucel N, Stuart GW, Maruff P, Wood SJ, Savage GR, Smith DJ, Crowe SF, Copolov DL, Velakoulis D, Pantelis C (2002) Paracingulate morphologic differences in males with established schizophrenia: a magnetic resonance imaging morphometric study. Biol Psychiatry 52:15–23

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Water Engineering Department, College of Agriculture, Shiraz University, Shiraz, Iran

    Seyed Adib Banimahd & Davar Khalili

Authors
  1. Seyed Adib Banimahd
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Davar Khalili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Davar Khalili.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Banimahd, S.A., Khalili, D. Factors Influencing Markov Chains Predictability Characteristics, Utilizing SPI, RDI, EDI and SPEI Drought Indices in Different Climatic Zones. Water Resour Manage 27, 3911–3928 (2013). https://doi.org/10.1007/s11269-013-0387-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-013-0387-z

Keywords

Search

Navigation