OJMH> Vol.4 No.3, July 2014

Stochastic Characteristics and Modelling of Monthly Rainfall Time Series of Ilorin, Nigeria

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ABSTRACT

The analysis of time series is essential for building mathematical models to generate synthetic hydrologic records, to forecast hydrologic events, to detect intrinsic stochastic characteristics of hydrologic variables as well to fill missing and extend records. To this end, this paper examined the stochastic characteristics of the monthly rainfall series of Ilorin, Nigeria vis-à-vis modelling of same using four modelling schemes. The Decomposition, Square root transformation-deseasonalisation, Composite, and Periodic Autoregressive (T-F) modelling schemes were adopted. Results of basic analysis of the stochastic characteristics revealed that the monthly series does not show any discernible presence of long-term trend, though there is a seeming inter-decadal annual variation. The series exhibits strong seasonality throughout its length, both in the moments and autocorrelation and significantly intermittent. Based on assessment of the respective models, the performance of the different modelling schemes can be expressed in this order: T-F > Composite > Square root transformation-Deseasonalised > Decomposition. Considering the results obtained, modelling of monthly rainfall series in the presence of serial correlation between months should be based on the establishment of conditional probability framework. On the other hand, in view of the inadequacy of these modelling schemes, because of the autoregressive model components in the coupling protocol, nonlinear deterministic methods such as Artificial Neural Network, Wavelet models could be viable complements to the linear stochastic framework.

Cite this paper

Edwin, A. and Martins, O. (2014) Stochastic Characteristics and Modelling of Monthly Rainfall Time Series of Ilorin, Nigeria. Open Journal of Modern Hydrology, 4, 67-79. doi: 10.4236/ojmh.2014.43006.

References

[1] Kottegoda, N.T. (1980) Stochastic Water Resources Technology. Macmillan Press Ltd., London, 2-3, 21, 112-113.
[2] Ramana, R.V., Krishna, B. and Kumar, S.R. (2013) Monthly Rainfall Prediction Using Wavelet Neural Network Analysis. Journal of Water Resources Management, 27, 3697-3711.
http://dx.doi.org/10.1007/s11269-013-0374-4
[3] Georgiou, E.F. and Krajewski, W. (1995) Recent Advances in Rainfall Modelling, Estimation, and Forecasting. US National Report to International Union of Geodesy and Geophysics, 1125-1137.
[4] Gupta, V. and Waymire, E. (1993) A Statistical Analysis of Mesoscale Rainfall as a Random Cascade. Journal of Applied Meteorology and Climatology, 32, 251-267.
http://dx.doi.org/10.1175/1520-0450(1993)032<0251:ASAOMR>2.0.CO;2
[5] Holley, R. and Waymire, E. (1992) Multifractal Dimensions and Scaling Exponents for Strongly Bounded Random Cascades. The Annals of Applied Probability, 2, 819-845.
http://dx.doi.org/10.1214/aoap/1177005577
[6] Pavlopoulos, H. and Kedem, B. (1992) Stochastic Modelling of Rain Rate Processes: A Diffusion Model. Communications in Statistics. Stochastic Models, 8, 397-420,
[7] Jimoh, O.D. and Webster, P. (1996) The Optimum Order of a Markov Chain Model for Daily Rainfall in Nigeria. Journal of Hydrology, 185, 45-69.
http://dx.doi.org/10.1016/S0022-1694(96)03015-6
[8] Gregory, J.M., Wigley, T.M.L. and Jones, P.D. (1992) Determining and Interpreting the Order of a Two State-Markov Chain: Application to Models of Daily Precipitation. Water Resources Research, 28, 1443-1446.
http://dx.doi.org/10.1029/92WR00477
[9] Koutsoyiannis, D. (1992) A Nonlinear Disaggregation Method with a Reduced Parameter Set for Simulation of Hydrologic Series. Water Resources Research, 28, 3175-3191.
http://dx.doi.org/10.1029/92WR01299
[10] Lovejoy, S. and Schertzer, D. (1990) Multifractals, Universality Classes and Satellite and Radar Measurements of Clouds and Rain Fields. Journal of Geophysical Research, 95, 2021.
http://dx.doi.org/10.1029/JD095iD03p02021
[11] Otache, M.Y., Ahaneku, I.E. and Mohammed, S.A. (2011) Parametric Linear Stochastic Modelling of Benue River flow Process. Open Journal of Marine Science, Scientific Research, 1-9.
[12] Delleur, J.W. and Kavvas, M.L. (1978) Stochastic Models for Monthly Rainfall Forecasting and Synthetic Generation. Journal of Applied Meteorology, 17, 1528-1536.
http://dx.doi.org/10.1175/1520-0450(1978)017<1528:SMFMRF>2.0.CO;2
[13] Bhakar, R.S., Singh, R.V., Chhajed, N. and Bansal, A.K. (2006) Stochastic Modelling of Monthly Rainfall at Kota Region. ARP Journal of Engineering and Applied Sciences, 1.
[14] Chebaane, M., Salas, J.D. and Boes, D.C. (1992) Product Autoregressive Process for Modelling Intermittent Monthly Streamflows. Water Resources Research, 28.
[15] Otache, M.Y., Ahaneku, I.E. and Mohammed, S.A. (2011) ARMA Modelling of Benue River Flow Dynamics: Comparative Study of PAR Model. Open Journal of Modern Hydrology, Scientific Research, 1, 1-9.

  
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