Water Resources Management

, Volume 22, Issue 10, pp 1395–1407 | Cite as

Stochastic Modelling of Rainfall in Humid Region of North East India

  • P. P. Dabral
  • Ashish Pandey
  • N. Baithuri
  • B. C. Mal


Rainfall is one of the fundamental components of the hydrological cycle as its accurate estimation is necessary for planning, designing and operation of water resources development programmes. In the present study, monthly stochastic model was developed using rainfall data for Doimukh (Itanagar), Arunachal Pradesh, India. The rainfall series was assumed to be composed of deterministic and stochastic components. The trend component was found to be non-significant. Fourier series analysis was used to identify the periodic component. Three harmonics were found significant. The stochastic component was modeled by fitting into auto regressive model of order 6. The Mean and standard deviation of the generated series were found to be close to the historical values. The value of absolute error, relative error, correlation coefficient and Nash–Sutcliffe coefficient indicated a high degree of model fitness to the observed data series.


Rainfall Stochastic models Time series Autoregressive models Humid region 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anderson OD (1976) Time series analysis and forecasting, the Box–Jenkins Approach. Butterwarth, LondonGoogle Scholar
  2. Bhakar SR, Singh R, Hari R (2006) Stochastic modeling of wind speed at Udaipur. Indian J Agril Engg 43(1):1–7Google Scholar
  3. Brockwell PJ, Davis RA (2002) Introduction to time series and forecasting, Springer International EditionGoogle Scholar
  4. Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. Mc Graw-Hill, New YorkGoogle Scholar
  5. Gupta RK, Kumar R (1994) Stochastic analysis of weekly evaporation values. Indian J Agril Engg 4(3–4):140–142Google Scholar
  6. Gupta M, Singh R (2004) Stochastic modeling of evaporation in arid region of India. Hydrol J (IAH) 27(3–4):1–13CrossRefGoogle Scholar
  7. Hipel KW, Mclead AI (1994) Times series modeling of water resources and environmental system. Elsevier, AmsterdamCrossRefGoogle Scholar
  8. Jha V, Singh R, Bhakar SR (2003) Stochastic modeling of soil moisture. J Agril Engg 40(4):51–56Google Scholar
  9. Kite G (1989) Use of time series analysis to detect climatic change. J Hydrol 111:256–279CrossRefGoogle Scholar
  10. Kottegoda NT (1980) Stochastic water resources technology. Macmillan Press, LondonGoogle Scholar
  11. Ljung GM, Box GEP (1978) On a measure of lack of fit in time series models. Biomerika 65:297–303CrossRefGoogle Scholar
  12. Mutreja KN (1986) Applied hydrology. Tata Mc Graw-Hill, New DelhiGoogle Scholar
  13. Salas JD, Delleur JW, Yevgevich V, Lane WL (1981) Applied modeling of hydrologic time series. Water Resources Publication, Littleton, ColoradoGoogle Scholar
  14. Rajkumar KN, Kumar D (2004) Stochastic modeling of daily rainfall for south west monsoon season of Baptla, Andhra Pradesh. Indian J Agril Engg 41(3):41–45Google Scholar
  15. Reddy KM, Kumar D (1999) Time series analysis of monthly rainfall for Bino watershed of Ramganga river. J Agril Engg (ISAE) 36(4):19–29Google Scholar
  16. Toth E, Drath A, Montanari A (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. J Hydrol 239:132–147CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • P. P. Dabral
    • 1
  • Ashish Pandey
    • 2
  • N. Baithuri
    • 1
  • B. C. Mal
    • 3
  1. 1.Department of Agricultural EngineeringNorth Eastern Regional Institute of Science and TechnologyItanagarIndia
  2. 2.Department of Water Resources Development and ManagementIndian Institute of TechnologyRoorkeeIndia
  3. 3.Department of Agricultural and Food EngineeringIndian Institute of TechnologyKharagpurIndia

Personalised recommendations