Abstract
Over the years, a number of prediction methods have been proposed for the evaluation of probability of hydrological–meteorological variables or drought indices. In this study, the precipitation data recorded in four stations of northwestern Iran over the period 1960–2014 were used to develop the time-series and genetic programming (GP) models. Comparison of the observed and predicted data showed that although both models have acceptable accuracy in predicting precipitation, the time-series models had lower errors than the GP models. So, the autoregressive and periodic autoregressive moving average models were chosen as the superior models for annual and monthly series, respectively. Therefore, the Standard Precipitation Index (SPI) and Z-Score Index (ZSI) were used to assess the drought conditions. According to the results, the SPI recognised a higher percentage of historical and prediction periods as drought conditions than ZSI. The validation of indices showed that the ZSI was more capable for detecting the drought and wetness conditions. The trend analysis of SPI and ZSI showed significant decreasing trends in different stations at all-time scales, except yearly in Urmia and all-time scales in Zanjan, which statically had no significant trend. In conclusion, given the current precipitation trends, the droughts are increasing in both severity and numbers.
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Ahmadi F, Dinpajhoh Y, Fakheri Fard A, Khalili K and Darbandi S 2015 Comparing nonlinear time series models and genetic programming for daily river flow forecasting (Case study: Barandouz-Chai River); J. Water Soil Conserv. 22(1) 151–169 (in Persian), http://jwsc.gau.ac.ir/article_2363.html.
Akhtari R, Morid S, Mahdian M H and Smakhtin V 2009 Assessment of areal interpolation methods for spatial analysis of SPI and EDI drought indices; Int. J. Climatol. 29 135–145, https://doi.org/10.1002/joc.1691
Alvisi S, Mascellani G, Franchini M and Bardossy A 2006 Water level forecasting through fuzzy logic and artificial neural network approaches; Hydrol. Earth Syst. Sci. 10 1–17, https://doi.org/10.5194/hess-10-1-2006.
American Meteorological Society 1997 Policy statement: Meteorological drought; Bull. Am. Meteorol. Soc. 78 847–849.
Aytek A and Asce M 2008 An application of artificial intelligence for rainfall runoff modeling; J. Earth Syst. Sci. 117(2) 145–155, 10.1007/s12040-008-0005-2.
Bazrafshan O, Salajegheh A, Bazrafshan J, Mahdavi M and Fatehi Maraj A 2015 Hydrological drought forecasting using ARIMA models (Case Study: Karkheh Basin); Ecopersia 3(3) 1099–1117, http://ecopersia.modares.ac.ir/article_14033.html.
Borelli A, DeFalco I, Della C A, Nicodemi M and Trautteur G 2006 Performance of genetic programming to extract the trend in noisy data series; Physica A 370 104–108, https://doi.org/10.1016/j.physa.2006.04.025.
Danandeh Mehr A, Kahya E and Yerdelen C 2014 Linear genetic programming application for successive-station monthly streamflow prediction; Comput. Geosci.-Uk. 70 63–72, https://doi.org/10.1016/j.cageo.2014.04.015.
Dogan S, Berktay A and Singh V P 2012 Comparison of multi-monthly rainfall-based drought severity indices, with application to semi-arid Konya closed basin, Turkey; J. Hydrol. 470–471 255–268, https://doi.org/10.1016/j.jhydrol.2012.09.003, https://doi.org/10.1016/j.wace.2016.11.005.
Edwards D C and Mckee T B 1997 Characteristics of 20th century drought in the United States at multiple time scales; Atmospheric Science Paper No. 634. Climatology Report No. 97(2), http://ccc.atmos.colostate.edu/edwards.pdf
Escalante-Sandoval C and Nuñez-Garcia P 2017 Meteorological drought features in northern and northwestern parts of Mexico under different climate change scenarios; J. Arid Land 9(1) 65–75, https://doi.org/10.1007/s40333-016-0022-y
Guven A 2009 Linear genetic programming for time-series modeling of daily flow rate; J. Earth Syst. Sci. 118(2) 157–173, https://doi.org/10.1007/s12040-009-0022-9.
Han P, Wang P, Zhang S and Zhu D 2010 Drought forecasting based on the remote sensing data using ARIMA models; Math. Comput. Model 51 1398–1403, https://doi.org/10.1016/j.mcm.2009.10.031.
Hassanzadeh Y, Abdi Kordani A and Fakheri Fard A 2012 Drought forecasting using genetic algorithm and conjoined model of neural network-wavelet; Bimon. J. Water Wastewater 23(3) 48–59 (in Persian), http://www.wwjournal.ir/article_1936_en.html.
Jain V K, Pandey R P, Jain M K and Byun H R 2015 Comparison of drought indices for appraisal of drought characteristics in the Ken River Basin; Weather Climate Extremes 8 1–11, https://doi.org/10.1016/j.wace.2015.05.002.
Javanmard S., Emamhadi M., BodaghJamali J., Didehvaras A. 2017 Spatial-temporal analysis of drought in Iran using SPI during a long-term period; Earth Sciences 6(2) 15–29. https://doi.org/10.11648/j.earth.20170602.12
Khalili K, Tahoudi M N, Mirabbasi R and Ahmadi F 2016 Investigation of spatial and temporal variability of precipitation in Iran over the last half century; Stoch. Env. Res. Risk A 30(4) 1205–1221, https://doi.org/10.1007/s00477-015-1095-4.
Krause P, Boyle D P and Base F 2005 Comparison of different efficiency criteria for hydrological model assessment; Adv. Geosci. 5 89–97, https://doi.org/10.5194/adgeo-5-89-2005.
Mahsin M, Yesmin A and Monira B 2012 Modeling rainfall in Dhaka division of Bangladesh using time series analysis; J. Math. Model. Appl. 1(5) 67–73, http://proxy.furb.br/ojs/index.php/modelling/article/view/2331.
Maity R, Suman M and Verma N K 2016 Drought prediction using a wavelet based approach to model the temporal consequences of different types of droughts; J. Hydrol. 539 417–428, https://doi.org/10.1016/j.jhydrol.2016.05.042.
Makkeasorn A, Chang N B and Zhou X 2008 Short-term streamflow forecasting with global climate change implications – A comparative study between genetic programming and neural network models; J. Hydrol. 352 336–354, https://doi.org/10.1016/j.jhydrol.2008.01.023.
Marco J B, Harboe R and Salas J D (eds) 2012 Stochastic hydrology and its use in water resources systems simulation and optimization, Vol. 237, Springer Science & Business Media, Netherlands, p. 473, https://doi.org/10.1007/978-94-011-1697-8.
Maybank J, Bonsai B, Jones K, Lawford R, O’brien E G, Ripley E A and Wheaton E 1995 Drought as a natural disaster; Atmos. Ocean 33(2) 195–222, https://doi.org/10.1080/07055900.1995.9649532.
McCuen R H 1989 Hydrologic analysis and design; Prentice Hall, Inc., Englewood cliffs, New Jersey.
McKee T, Doesken N and Kleist J 1993 The relationship of drought frequency and duration to time scales; In: Proceedings of the 8th, Boston, pp. 179–184, citeulike-article-id:14027580.
Meher J and Jha M 2013 Time-series analysis of monthly rainfall data for the Mahanadi River Basin, India; Sci. Cold Arid Regions 5(1) 0073–0084, https://doi.org/10.3724/SP.J.1226.2013.00073
Modarres R 2007 Streamflow drought time series forecasting; Stoch. Environ. Res. Risk A 21 223–233, https://doi.org/10.1007/s00477-006-0058-1.
Momani M and Naill P E 2009 Time series analysis model for rainfall data in Jordan: Case study for using time series analysis; Am. J. Environ. Sci. 5(5) 599–604, https://doi.org/10.3844/ajessp.2009.599.604.
Morid S, Smakhtin V and Moghaddasi M 2006 Comparison of seven meteorological indices for drought monitoring in Iran; Int. J. Climatol. 26 971–985, https://doi.org/10.1002/joc.1264.
Osmani L 2009 SPI application for investigating of drought frequency, intensity and extent in northwest of Iran; In: The second national conference on drought effects/management (DEM), Agricultural and Natural Resources, Research Center, Isfahan, Iran, pp. 20–21, May, 2009 (in Persian).
Parsafar N and Maroufi S 2009 Meteorological drought indices efficiency in drought risk management of Orumiyeh area; In: Iranian national conference on water crisis in agriculture and natural resources, Azad Islamic University of Shahre Rey, Iran, 4 November 2009. In Persian.
Paulo A A, Ferreira E, Coelho C and Pereira L S 2005 Drought class transition analysis through Markov and loglinear models, an approach to early warning; Agric. Water Manag. 77(1–3) 59–81, https://doi.org/10.1016/j.agwat.2004.09.039.
Rajurkara M, Kothyarib U and Chaube U 2004 Modeling of the daily rainfall-runoff relationship with artificial neural network; J. Hydrol. 285 96–113, https://doi.org/10.1016/j.jhydrol.2003.08.011.
Saada N 2014 Time series modeling of monthly rainfall in arid areas: Case study for Saudi Arabia; Am. J. Environ. Sci. 10(3) 277, https://doi.org/10.3844/ajessp.2014.277.282.
Salahi B., Faridpour M. 2016 Spatial analysis of climatic drought in North West of Iran using spatial autocorrelation statistics. Jsaeh 3(3) 1–20, http://jsaeh.khu.ac.ir/article-1-2617-en.html
Salas J D, Delleur J W, Yevjevich V and Lane W L 1980 Applied modeling of hydrologic time series; Water Resource Publications, Littleton, CO, USA, 484 Papers.
Shao Q, Wong H, Li M and Ip W C 2009 Streamflow forecasting using functional-coefficient time series model with periodic variation; J. Hydrol. 368(1–4) 88–95, https://doi.org/10.1016/j.jhydrol.2009.01.029.
Smakhtin V U and Hughes D A 2007 Automated estimation and analyses of meteorological drought characteristics from monthly rainfall data; Environ. Model. Softw. 22(6) 880–890, https://doi.org/10.1016/j.envsoft.2006.05.013.
Suryabhagavan K V 2017 GIS-based climate variability and drought characterization in Ethiopia over three decades; Weather Clim. Extrem. 15 11–23.
Wei W S W 2006 Time series analysis – univariate and multivariate methods; (2nd edn), Pearson publication, USA, p. 614, https://doi.org/10.1093/oxfordhb/9780199934898.013.0022.
Wu H, Hayes M J, Weiss A and Hu Q I 2001 An evaluation of the standardized precipitation index, the China-Z Index and the statistical Z-score; Int. J. Climatol. 21 745–758, https://doi.org/10.1002/joc.658.
Zahedi M, Sarisarraf B and Jame’Ee J 2006 Rain modeling in Tabriz and Oroumiyeh stations; J. f. Geography. Reg. Dev. 4(7) 1–16, https://doi.org/10.22067/geography.v4i7.4164A.
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The authors wish to thank the I.R. of Iran Meteorological Organisation for useful information and data. Also, we would like to thank the Natural Resources and Earth Sciences Faculty of University of Kashan for providing helpful facilities that have led to significant improvement in this research results.
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Omidvar, E., Tahroodi, Z.N. Evaluation and prediction of meteorological drought conditions using time-series and genetic programming models. J Earth Syst Sci 128, 73 (2019). https://doi.org/10.1007/s12040-019-1103-z
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DOI: https://doi.org/10.1007/s12040-019-1103-z