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Selection of the best probability models for daily annual maximum rainfalls in Egypt

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Abstract

Twelve commonly used probability distributions are evaluated to identify the most suitable model that could provide accurate extreme rainfall estimates in Egypt. Three popular parameter estimation methods are applied: the method of moments, L-moments and maximum likelihood. The performance of the models is evaluated based on several numerical and graphical goodness-of-fit criteria. The proposed procedure is applied to annual maximum daily rainfall data from a network of 31 stations located in Egypt. The results indicate that no single distribution performed the best at all stations. Log-Normal, Log-Pearson Type III and Exponential are the top three distributions for the frequency analysis of daily annual extreme rainfalls in Egypt, i.e. they are selected as the “optimum” models for 23%, 19% and 19% of the total stations, respectively. In contrast, the distributions: Normal, Gumbel, Logistic and Generalized Logistic are not suitable for describing the extreme rainfalls in the country. The performances of both L-moments and maximum likelihood methods are almost equal and much better than that of the method of moments. Additionally, Depth-Duration-Frequency curves were established for 18 stations by using the “optimum” model, which can support the design of hydraulic structures. The findings from this study would be helpful for rainfall frequency analysis in similar arid countries.

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References

  • Acquah HG (2010) Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of an asymmetric price relationship. J Dev Agric Econ 2(1):1–6

    Google Scholar 

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723

    Article  Google Scholar 

  • Ball J, Babister M, Nathan R, Weeks W, Weinmann E, Retallick M, Testoni I (2016) Australian rainfall and runoff: a guide to flood estimation. Commonwealth of Australia

  • Beskow S, Caldeira TL, Millo CR, Faria LC, Guedes HAS (2015) Multiparameter probability distributions for heavy rainfall modeling in extreme southern Brazil. Journal of Hydrology: Regional Studies. Elsevier B.V., 4(PB), pp. 123–133. doi: https://doi.org/10.1016/j.ejrh.2015.06.007

  • Bonnin GM, Martin D, Lin B, Parzyok T, Yekta M, Riley D (2006) ‘Precipitation-frequency atlas of the United States, Volume 1, Version 4.0: Semiarid Southwest (Arizona, Southeast California, Nevada, New Mexico)’, NOAA Atlas 14

  • Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer-Verlag New York, New York, USA

    Google Scholar 

  • Burnham KP, Anderson DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res 33:261–304

    Article  Google Scholar 

  • Busemeyer JR, Diederich A (2014) Chapter 4 - Estimation and testing of computational psychological models, in Neuroeconomics (Second Edition), pp. 49–61

  • Coles S (2001) An introduction to statistical modeling of extreme values. Springer, London

    Book  Google Scholar 

  • Cunnane C (1989) ‘Statistical distributions for flood frequency analysis’, Operational hydrological Report No. 5/33, World Meteorological Organization (WMO), Geneva, Switzerland

  • EEAA (2016) Egypt third national communication under the United Nations framework convention on climate change

  • El Kenawy A, López-Moreno JI, Vicente-Serrano SM, Morsi F (2010) Climatological modeling of monthly air temperature and precipitation in Egypt through GIS techniques. Clim Res 42:161–176. https://doi.org/10.3354/cr00871

    Article  Google Scholar 

  • Elmenoufy HM, Morsy M, Eid MM, El Ganzoury A, Wahab MMA (2017) Towards enhancing rainfall projection using bias correction method: case study Egypt. IJSRSET 6(3):187–194

    Google Scholar 

  • Gado TA (2020). Statistical behavior of rainfall in Egypt. In: Negm A. (eds) Flash floods in Egypt. Advances in science, technology & innovation (IEREK Interdisciplinary Series for Sustainable Development). Springer, Cham. https://doi.org/10.1007/978-3-030-29635-3_2

  • Gado TA, El-Agha DE (2020) Feasibility of rainwater harvesting for sustainable water management in urban areas of Egypt. Environ Sci Pollut Res 27:32304–32317. https://doi.org/10.1007/s11356-019-06529-5

    Article  Google Scholar 

  • Gado TA, Nguyen VTV (2015) Comparison of homogenous region delineation approaches for regional flood frequency analysis at ungauged sites. J Hydrol Eng (ASCE), 21 (3), pp. 04015068-1: 04015068-10

  • Gado TA, Nguyen VTV (2016a) An at-site flood estimation method in the context of nonstationarity. I: A simulation study. J Hydrol Eng 535:710–721

    Article  Google Scholar 

  • Gado TA, Nguyen VTV (2016b) An at-site flood estimation method in the context of nonstationarity. II: Statistical analysis of floods in Quebec. J Hydrol Eng 535:722–736

    Article  Google Scholar 

  • Gado TA, Nguyen VTV (2016c) Regional estimation of floods for ungauged sites using partial duration series and scaling approach. J Hydrol Eng (ASCE) 21(12). https://doi.org/10.1061/(ASCE)HE.1943-5584.0001439

  • Gado TA, El-Hagrsy RM, Rashwan IMH (2019) Spatial and temporal rainfall changes in Egypt. Environ Sci Pollut Res 26:28228–28242. https://doi.org/10.1007/s11356-019-06039-4

    Article  Google Scholar 

  • Greenwood JA, Landwehr JM, Matalas NC, Wallis JR (1979) Probability weighted moments: definition and relation to parameters of several distributions expressed in inverse form. Water Resour Res 15(5):1049–1064

    Article  Google Scholar 

  • Griffis VW, Stedinger JR (2007) Evolution of flood frequency analysis with bulletin 17. J Hydrol Eng 12(3):283–297

    Article  Google Scholar 

  • Haddad K, Rahman A (2011) Selection of the best fit flood frequency distribution and parameter estimation procedure: a case study for Tasmania in Australia. Stoch Env Res Risk A 3:415–428. https://doi.org/10.1007/s00477-010-0412-1

    Article  Google Scholar 

  • Hershfield, D.M. (1962) Rainfall frequency atlas of the United States for durations from 30 minutes to 24 hours and return periods from 1 to 100 years. U.S. Weather Bureau Technical Paper 40, Washington, D.C

  • Hosking JRM (1986) The theory of probability weighted moments. Research Report RC12210, IBM Research Division, Yorktown Heights, N.Y

  • Hosking JRM (1990) L-moments: analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society Series 52(1):105–124

    Google Scholar 

  • Kaźmierczak B, Kotowski A (2015) The suitability assessment of a generalized exponential distribution for the description of maximum precipitation amounts. J Hydrol 525:345–351. https://doi.org/10.1016/j.jhydrol.2015.03.063

    Article  Google Scholar 

  • Koutsoyiannis D, Kozonis D, Manetas A (1998) A mathematical framework for studying rainfall intensity-duration-frequency relationships. J Hydrol 206:118–135

    Article  Google Scholar 

  • Lang M, Bobe B (1999) Towards operational guidelines for over-threshold modeling. J Hydrol 225:103–117

    Article  Google Scholar 

  • Lee SH, Maeng SJ (2003) Frequency analysis of extreme rainfall using L-moment. Irrig Drain 230:219–230. https://doi.org/10.1002/ird.090

    Article  Google Scholar 

  • Mahdavi M, Osati K, Sadeghi SAN, Karimi B, Mobaraki J (2010) Determining suitable probability distribution models for annual precipitation data ( a case study of Mazandaran and Golestan Provinces ). Journal of Sustainable Development 3(1):159–168

    Article  Google Scholar 

  • Mamoon A, Rahman A (2016) Selection of the best fit probability distribution in rainfall frequency analysis for Qatar. Nat Hazards. Springer Netherlands 86(1):281–296. https://doi.org/10.1007/s11069-016-2687-0

    Article  Google Scholar 

  • Nashwan MS, Shahid S, Rahim NA (2018) Unidirectional trends in annual and seasonal climate and extremes in Egypt. Theor Appl Climatol 136:457–473. https://doi.org/10.1007/s00704-018-2498-1

    Article  Google Scholar 

  • Natural Research Council of Canada (1989) Hydrology of floods in Canada: a guide to planning and design. Ottawa, 245

  • Nguyen VTV, Mayabi A (1991) Probabilistic analysis of summer daily rainfall for the Montreal Region. Canadian Water Resources Journal 16(1):65–80. https://doi.org/10.4296/cwrj1601065

    Article  Google Scholar 

  • Nguyen TH, Outayek S, Lim SH, Nguyen VTV (2017) A systematic approach to selecting the best probability models for annual maximum rainfalls – a case study using data in Ontario (Canada). J Hydrol. Elsevier B.V. 553:49–58. https://doi.org/10.1016/j.jhydrol.2017.07.052

    Article  Google Scholar 

  • Ogunlela AO (2001) Stochastic analysis of rainfall events in Ilorin, Nigeria. Journal of Agricultural Research and Development 1:39–50

    Google Scholar 

  • Olofintoye OO, Sule BF, Salami A (2013) Best–fit probability distribution model for peak daily rainfall of selected Cities in Nigeria. J Chem Inf Model 53(3):1689–1699. https://doi.org/10.1017/CBO9781107415324.004

    Article  Google Scholar 

  • Parida BP (1999) Modelling of Indian summer monsoon rainfall using a four-parameter Kappa distribution. Int J Climatol 19:1389–1398

    Article  Google Scholar 

  • Park JS, Jung HS (2002) Modelling Korean extreme rainfall using a Kappa distribution and maximum likelihood estimate. Theor Appl Climatol 72:55–64

    Article  Google Scholar 

  • Rahman AS, Rahman A, Zaman MA, Haddad K, Ahsan A, Imteaz M (2013) A study on selection of probability distributions for at-site flood frequency analysis in Australia. Nat Hazards 69:1803–1813. https://doi.org/10.1007/s11069-013-0775-y

    Article  Google Scholar 

  • Rao AR, Hamed KH (2000) Flood frequency analysis. CRC Press, Boca Raton, London, p 356

    Google Scholar 

  • Salinas JL, Castellarin A, Kohnová S, Kjeldsen TR (2014) Regional parent flood frequency distributions in Europe – Part 2: Climate and scale controls. Hydrol Earth Syst Sci 18(11):4391–4401

    Article  Google Scholar 

  • Sen Z, Eljadid AG (1999) Rainfall distribution functions for Libya and rainfall prediction. Hydrol Sci J 4(5):665–680

    Article  Google Scholar 

  • Soro, G. E., Goula, T. A., Kouassi, F. W., and Srohourou, B. (2010) Update of intensity-duration-frequency curves for precipitation of short durations in tropical area of West Africa (Cote D’ivoire). Journal of applied sciences, pp. 704–715.

  • Tao DQ, Nguyen TV, Bourque A (2002) On selection of probability distributions for representing extreme precipitations in Southern Quebec. Annual Conference of the Canadian Society for Civil Engineering, pp. 1–8

  • Topaluglu F (2002) Determining suitable probability distribution models for flow and precipitation series of the Seyhan River Basin. Turk J Agric For 26:187–194

    Google Scholar 

  • Vivekanandan N (2014) Rainfall frequency analysis using L-moments of probability distributions. International Journal of Computer Application and Engineering Technology 3(3):248–256

    Google Scholar 

  • Wdowikowski M, Kaźmierczak B, Ledvinka O (2016) Maximum daily rainfall analysis at selected meteorological stations in the upper Lusatian Neisse River basin. Meteorology Hydrology and Water Management 4(1):53–63. https://doi.org/10.26491/mhwm/63361

    Article  Google Scholar 

  • Wdowikowski M, Kotowski A, Dabek PB, Kazmierczak B (2017) Probabilistic approach of the Upper and Middle Odra basin daily rainfall modeling. E3S Web of Conferences 17, 00096, doi: https://doi.org/10.1051/e3sconf/20171700096.

  • Zalina MD, Desa MN, Nguyen VTV, Kassim AHM (2002) Selecting a probability distribution for extreme rainfall series in Malaysia. Water Sci Technol 45(2):63–68

    Article  Google Scholar 

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Conceptualization, Tamer A. Gado and Bakenaz A. Zeidan; methodology, Abeer M. Salama and Tamer A. Gado; writing—original draft preparation, Abeer M. Salama; writing—review and editing, Tamer A. Gado and Bakenaz A. Zeidan. All authors have read and agreed to the published version of the manuscript.

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Gado, T.A., Salama, A.M. & Zeidan, B.A. Selection of the best probability models for daily annual maximum rainfalls in Egypt. Theor Appl Climatol 144, 1267–1284 (2021). https://doi.org/10.1007/s00704-021-03594-0

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