##plugins.themes.bootstrap3.article.main##

The choice of optimum probability distribution model that would accurately simulate flood discharges at a particular location or region has remained a challenging problem to water resources engineers. In practice, several probability distributions are evaluated, and the optimum distribution is then used to establish the quantile - probability relationship for planning, design and management of water resources systems, risk assessment in flood plains and flood insurance. This paper presents the evaluation of five probability distributions models: Gumbel (EV1), 2-parameter lognormal (LN2), log pearson type III (LP3), Pearson type III(PR3), and Generalised Extreme Value (GEV) using the method of moments (MoM) for parameter estimation and annual maximum series of five hydrological stations in the lower Niger River Basin in Nigeria. The choice of optimum probability distribution model was made on five statistical goodness – of – fit measures; modified index of agreement (Dmod), relative root mean square error (RRMSE), Nash – Sutcliffe efficiency (NSE), Percent bias (PBIAS), ratio of RMSE and standard deviation of the measurement (RSR), and probability plot correlation coefficient (PPCC). The results show that GEV is the optimum distribution in 3 stations, and LP3 in 2 stations. On the overall GEV is the best – fit distribution, seconded by PR3 and thirdly, LP3. Furthermore, GEV simulated discharges were in closest agreement with the observed flood discharges. It is recommended that GEV, PR3 and LP3 should be considered in the final selection of optimum probability distribution model in Nigeria.

Downloads

Download data is not yet available.

References

  1. UNISDR (United Nations International Strategy for Disaster Reduction), 2011. Revealing risk, redefining development. Geneva: UNISDR.
     Google Scholar
  2. Kundzewicz, Z.W., et al., 2013. Flood risk and climate change: global and regional perspectives. Hydrological Sciences Journal, 59(1), 1 – 28.
     Google Scholar
  3. Panda, A. and Amaratunga, D. (2019) Resilience Cities, Oxford Research Encyclopedia of Hazard Science, DOI:10.1093/acrefore/ 9780199389407.013.321.
     Google Scholar
  4. Smithers, J.C. (2012). Methods for design flood estimation in South Africa. http.//dx.doi.org/10.4314/wsa.v38i4.19, Water SA Vol.38 No.4, pp.633 – 644.
     Google Scholar
  5. Strupczewski, W.G., Singh, V.P., and Weglarczyk, S., 2002.Asymptotic bias of estimation methods caused by the assumption of false probability distribution. Journal of Hydrology, 258 (1–4), 122–148. doi:10.1016/S0022-1694(01)00563-7.
     Google Scholar
  6. He, J., Anderson, A., and Valeo, C (2015), Bias compensation in flood frequency analysis, Hydrological Sciences Journal, 60(3) 381 – 397. http://dx.doi.org/10.1080/02626667.2014.885651.
     Google Scholar
  7. EM 1110-2-1450(1994). Hydrologic Frequency Estimates, U.S. Army Corps of Engineers, Washington, DC 20314-1000.
     Google Scholar
  8. Khaliq, M.N., Ouarda, T.B.M.J., Ondo, J.-C., Gachon, P., and Bobee, B. (2006). Frequency analysis of a sequence of dependent and/or non-stationary hydro – meteorological observations: A review, Journal of Hydrology, 329, pp.534–552.
     Google Scholar
  9. WMO: No.718, 1989 (World Meteorological Organization). (1989). Statistical distributions for flood frequency analysis. Geneva: World Meteorological Organization.
     Google Scholar
  10. Rahman A.S, Rahman, A. Zaman M.A., Haddad K., Ahsan A., Imteaz M. (2013), A study on selection of probability distributions for at – site flood frequency analysis in Australia, Nat Hazards (2013) 69:1803 -1813. DOI 10.1007/s11069-013-0775-y.
     Google Scholar
  11. Cunnane, C., (1989). Statistical Distribution for Flood Frequency Analysis. Operational Hydrol. Rep. 33, World Meteorological Organisation, Geneva.
     Google Scholar
  12. Onoz, B. and Bayazit, M. (1995), Best-fit distributions largest available flood samples, Journal of Hydrology, 167 (1995), 195–208.
     Google Scholar
  13. Vogel, R.M. and Wilson, I. (1996). Probability Distribution of Annual, Maximum, Mean, and Minimum Streamflows in the United States. Journal of Hydrologic Engineering, Vol.1, No. 2, pp. 69–76.
     Google Scholar
  14. FLOODFREQcost Action ES0901, Review of Applied-Statistical Methods for Flood – Frequency Analysis in Europe (WG2).
     Google Scholar
  15. Abida H. and Ellouze M (2007), Probability distribution of flood flows in Tunisia, Hydrol. Earth Syst. Sci. Discuss., 4, 957–981.
     Google Scholar
  16. Ehiorobo J.O. and Akpejiori I.J (2016), Flood Frequency Analysis of River Niger at Agenebode, Edo State, Nigeria, Journal of the Nigerian Association of Mathematical Physics, Volume 38, pp.309–318.
     Google Scholar
  17. Izinyon, O.C., and Ajumuka, H.N., (2013). Probability distribution models for flood prediction in Upper Benue River Basin- Part II. Civil and Environmental Research, Vol. 3, No.2, pp.62–74.
     Google Scholar
  18. Ibrahim U.A, Yadima S.G, Nur Alkali A (2016) Flood Frequency Analysis at Hadejia River in Hadejia-Jama’are River Basin, Nigeria, Civil and Engineering Research, Vol.8. No.9., ISSN 2224-5790(Paper) ISSN 2225-0514(Online).
     Google Scholar
  19. Mamman M.J, Otache Y.M, Ibrahim J, Shaba M.I (2017), Evaluation of Best –Fit Probability Distribution Models for the Prediction of Inflows of Kanji Reservoir, Niger State, Nigeria, Air, Soil and Water Research, Volume 10: 1-7, DOI:10.1177/117862211768 1034.
     Google Scholar
  20. Rao A.A and Hamed K.H (2000), FLOOD FREQUENCY ANALYSIS, CRC Press, ISBN 0-412-55280-9.
     Google Scholar
  21. VAN GELDER, P.H.A.J.M. WANG, W., and VRIJLING, J.K. (2007). Statistical Estimation Methods for Extreme Hydrological Events in Vasiliev, O.F et al. (eds.) Extreme Hydrological Events: New Concepts for Security, 199-252. 2007 Springer.
     Google Scholar
  22. Son, K., Lin, L., Band, L., and Owens, E.M. (2019). Modelling the interaction of climate, forest ecosystem, and hydrology to estimate catchment dissolved organic carbon export. Hydrological processes. 2019; 33:1448-1464.https://doi.org/10.1002/hyp.13412
     Google Scholar
  23. Moriasi, D.N., Gitau, M.W., Pai, N., and Daggupati. P. (2015). Hydrologic and Water Quality Models:Performance Measures and Evaluation Criteria. Transactions of the American Society of Agricultural and Biological Engineers, Vol. 58(6): 1763-1785.
     Google Scholar
  24. AMEC Environmental & Infrastructure (2014). Frequency Analysis Procedures for Stormwater Design Manual, CW2138.
     Google Scholar
  25. Karim, M.D.and Chowdhury (1995). A comparison of four distributions used in flood frequency analysis in Bangladesh. Hydrological Sciences Journal, 40(1), pp.55-66.
     Google Scholar
  26. Amirataee, B. and Montaseri, M. (2013). Evaluation of L-Moment and PPCC Method to Determine the Best Regional Distribution of Monthly Rainfall Data: Case Study Northwest of Iran. Journal of Urban and Environmental Engineering, v.7, n.2. pp. 247–252.
     Google Scholar
  27. Amirataee, B., Montaseri, M. and Rezael, H. (2014). Assessment of Goodness of Fit Methods in Determining the Best Regional Probability Distribution of Rainfall Data. International Journal of Engineering, IJE Transactions A: Basics Vol. 27, No. 10, pp. 1537–1546.
     Google Scholar
  28. Ahn, H., Kim, S., Lee, J., and Heo, J.-H. (2020). Regression equations of probability plot coefficient test statistics using machine learing, EGU General Assembly 2020. https://doi.org/10.5194/egusphere-egu2020-12315.
     Google Scholar
  29. Chen, X., Shao, Q., Xu, C-Yu., Zhang, J., Zhang, L., and Ye, C. (2017). Comparative Study on the Selection Criteria for Fitting Flood Frequency Distribution Models with Emphasis on Upper – Tail Behavoiur, water 2017, 9, 320; doi:10.3390/w9050320.
     Google Scholar
  30. Bilau A. A, Witt E, Lill I, Bustani S. A. (2012), Housing Reconstruction Following the 2012 Nigerian Floods: Was it Built Back Better? in Prins, M., Wamelink, H., Giddings, B., Ku,K., and Feenstra, M.(Eds.)(2016).Proceeding of the CIB World Building Congress 2016: Volume II-Environmental opportunities; Constructing Commitment and Acknowledging Human Experiences .(Tampere University of Technology. Department of Civil Engineering. Construction Management and economics. Report; Vol. 18). Tampere University of Technology.
     Google Scholar
  31. Naghettini, M (ed). Fundamentals of Statistical Hydrology, ISBN 978 – 3 – 319 – 43561-9, Springer.
     Google Scholar
  32. Krause, P., Boyle, D.P., and Base, F. (2005). Comparison of different efficiency criteria for hydrological model assessment, advances in Geosciences, 5, pp.89–97.
     Google Scholar
  33. Willmott, C.J., Robeson, S.M. and Matsuura, K. (2012). Short Communication: A refined index of model -performance. Int. J. Climatol. 32: 2088–2094(2012).
     Google Scholar
  34. Tao, D.Q., Nguyen, V-T-V and Bourque, A. (2002). On selection of probability distributions for representing extreme precipitation in southern Quebec. Annual Conference of the Canadian Society for Civil Engineering.
     Google Scholar
  35. Gupta, H.V., Sorooshian, S, and Yapo, P.O. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel calibration. J. hydrologic Eng. 4(2): 135–143.
     Google Scholar
  36. IN-NA, N. and Ngugen, V.-T.-V. (1989). An Unbiased Plotting Position Formula for the General Extreme Value Distribution. Journal of hydrology, 106(1989), pp.193 – 209.
     Google Scholar
  37. WMO168_Ed2009_Vol._II_Ch5_Up2008_en, Chapter 5: Extreme Value Analysis.
     Google Scholar
  38. Stedinger, J.R., Vogel, R.M. and Foufoula-Georgiou, E., 1993. Frequency analysis of extreme events. Handbook of Hydrology, New York, USA.
     Google Scholar
  39. Naghavi, B. and Yu Xin, F. (1996). Selection of Parameter- Estimation Method for LP3 Distribution. Journal of Irrigation and Drainage Engineering, Vol. 122, No. pp.24–30.
     Google Scholar
  40. [40] Kite, G. W. (1977). Frequency and risk analyses in hydrology. WaterResources Publications, 303, https://books.google.se/books?id=WZt-AAAAIAAJ.
     Google Scholar
  41. Wilson, E.B. and Hilferty, M.M. (1931). “The distribution of Chi-squre”, Proceedings. National Academic of Science (New York), 17(12): 684- 688.
     Google Scholar
  42. Abramowitz, M. and Stegun, I.A. (1965). Handbook of Mathematical Functions. Dover Publications, New York.
     Google Scholar
  43. ASCE. 1993. Criteria for evaluation of watershed models. J. Irrigation Drainage Eng.119(3): 429–442.
     Google Scholar
  44. Haktanir, T., (1992). Comparison of various flood frequency distributions using annual flood peaks data of rivers in Anatolia, Journal of Hydrology, 136(1992), 1–31.
     Google Scholar