International Journal of Agricultural Sciences

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Probability Analysis for Prediction of Annual Maximum Rainfall of One to Five Consecutive Months for Sultanpur Region


Affiliations
1 Vaugh Institute of Agricultural Engineering and Technology (SHUATS), Allahabad (U.P.), India
     

   Subscribe/Renew Journal


The present study was conducted for probability analysis of 17th years (1994-2010) with the prime objective for prediction of annual maximum rainfall of one to five consecutive months Sultanpur region. The maximum rainfall values were estimated by proposed prediction models viz., Gumbel, Log Pearson Type III and Log Normal. The predicted values were compared with observed values and correlation between the predicted and observed values was also established. Rainfall data had been in the above distributions and their corresponding rainfall events were estimated at 5.5, 11.5, 6.6, 33.3 and 50 per cent probabilities level. The goodness of fit models was tested by chi-square. The comparison between the measured and predicted maximum value of rainfall clearly shows that the developed model can be efficiently used for the prediction of rainfall. The statistical comparison by chi-square test for goodness of fit clearly indicates that the Gumbel distribution was found to be best model for predicting two, three, four and five consecutive months annual maximum rainfalls (mm) while Log Pearson types III are fairly close to observed for one and four consecutive months annual maximum rainfall (mm). Rainfall prediction by Log Normal distribution shows very close relation to the observed rainfall for one consecutive months annual maximum rainfall (mm).

Keywords

Probability Analysis, Prediction, Annual Maximum Rainfall.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Agarwal, M. C., Katiyar, V. S. and Babu, R. (1988). Probability analysis of annual maximum daily rainfall of U.P. Himalaya. Indian J. Soil Cons., 16 (1): 35-45.

  • Bhatt, V.K., Tiwari, A.K. and and Sharma, A.K. (1996). Probability models for prediction of annual maximum daily rainfall for Datia. Indian J. Soil Cons., 24(1): 25-27.

  • Chow, V.T. (1951). A generalized formula for hydrologic frequency analysis. Transactions American Geophysical Union, 32 (2) : 231-237.

  • Chow, V. T., Maidment, David R. and Mays and Larry W. (1988). Applied hydrology, Mc – Graw – Hills., International Edition Civil engineering Series, pp. 250-405.

  • Gumbel, E.J. (1954). Statistical theory of extreme values and some practical applications. Applied Mathematics Series. 33 (1st Ed.). U.S. Department of Commerce, National Bureau of Standards.

  • Hogg, R.V. and Tanis E. A. (1977). Probability and statistical inference. Macmilan Publishing co. Inc., New York, U.S.A.

  • Kite, G. W. (1977). Frequency and risk analysis in hydrology, water resources. Publications, Fort Collins Colorado (USA).

  • Mohanty, S., Marathe, R. A. and Singh, S. (1999). Probability analysis of annual maximum daily rainfall for Amravati. J. Soil &Water Cons., 43 (1): 15-17.

  • Mohanty, S., Marathe, R. A. and Singh, S. (2000). Probability models for prediction of annual maximum daily rainfall for Nagpur. J. Soil &Water Cons., 44: 38-40.

  • Mohanty, S., Marthe, R. and Singh, S. (2001).Meteorological drought analysis based on rainfall data of Nagpur. J. Soil & Water Cons., 45 (1): 1-5.

  • Mohanty, S., Marathe, R. A. and Singh, S. (2001). Rainfall characteristics of vidarbha region, Indian J. Soil Cons., 28 (1): 18-21.

  • Prakash, C. and Rao, D. H. (1986). Frequency analysis of rainfall data for crop planning- Kota, India, J. Soil Cons., 14(20): 23-26.

  • Ray, C. R., Senapathi, P. C. and Lal, R. (1987). Investigation of drought from rainfall data at Gopalpur (Orissa).Indian.J.Soil Cons., 15 (1) :15-19.

  • Rizvi, R. H., Singh, R., Yadav, R.S., Tewari, R.K., Dadhwal, K. S. and Solanki, K. R. (2001). Probability analysis of annual maximum daily rainfall for bundelkhand region of Uttar Pradesh, Indian J. Soil Cons. 29 (3) : 259-262.

  • Singh, M., Bhattacharya, A.K., Sarkar, T. K. and Singh, C. (1992). Consecutive days rainfall prediction from one days rainfall. Indian J. Agric. Engg., 2(3): 192-196.

  • Singh, R.K. (2001). Probability analysis for prediction of maximum daily rainfall of eastern Himalaya (Sikkim Mid Hills). Indian. J. Soil Cons., 29 (3) : 263-265.

  • Subramanya, K. (1984). Engineering hydrology, Tata Mc Graw-Hill Publishing Company Ltd. New Delhi, India, pp. 242253.

  • Upadhyaya, A. (1997). A technique to predict consecutive days maximum rainfall using on day maximum rainfall. Indian J. Soil Cons., 25 (1) : 24-27.

  • Upadhyaya, A. and Singh, S.R. (1998). Estimation of consecutive days maximum rainfall by various methods and their comparison. Indian J. Soil Cons., 26 (3): 193-201.

  • Weibull’s, W. (1939). Statistical theory of the strength of materials. Ing. Vetenskapsakad. Handl, Stockholm, 151: 15.


Abstract Views: 306

PDF Views: 0




  • Probability Analysis for Prediction of Annual Maximum Rainfall of One to Five Consecutive Months for Sultanpur Region

Abstract Views: 306  |  PDF Views: 0

Authors

Anurag Patel
Vaugh Institute of Agricultural Engineering and Technology (SHUATS), Allahabad (U.P.), India
Raj Kumar Verma
Vaugh Institute of Agricultural Engineering and Technology (SHUATS), Allahabad (U.P.), India

Abstract


The present study was conducted for probability analysis of 17th years (1994-2010) with the prime objective for prediction of annual maximum rainfall of one to five consecutive months Sultanpur region. The maximum rainfall values were estimated by proposed prediction models viz., Gumbel, Log Pearson Type III and Log Normal. The predicted values were compared with observed values and correlation between the predicted and observed values was also established. Rainfall data had been in the above distributions and their corresponding rainfall events were estimated at 5.5, 11.5, 6.6, 33.3 and 50 per cent probabilities level. The goodness of fit models was tested by chi-square. The comparison between the measured and predicted maximum value of rainfall clearly shows that the developed model can be efficiently used for the prediction of rainfall. The statistical comparison by chi-square test for goodness of fit clearly indicates that the Gumbel distribution was found to be best model for predicting two, three, four and five consecutive months annual maximum rainfalls (mm) while Log Pearson types III are fairly close to observed for one and four consecutive months annual maximum rainfall (mm). Rainfall prediction by Log Normal distribution shows very close relation to the observed rainfall for one consecutive months annual maximum rainfall (mm).

Keywords


Probability Analysis, Prediction, Annual Maximum Rainfall.

References