GRD Journals | Global Research and Development Journal for Engineering | Emerging Research and Innovations in Civil Engineering (ERICE - 2019) | February 2019
e-ISSN: 2455-5703
Estimation of Annual One Day Maximum Rainfall using Probability Distributions for Waghodia Taluka, Vadodara 1Pranav
B. Mistry 2T. M. V. Suryanarayana Research Scholar 2Associate Professor 1,2 Department of Water Resources Engineering and Management Institute 1,2 The Maharaja Sayajirao University of Baroda, Samiala-391410, India 1
Abstract Rainfall is an infrequent and an important hydrological parameter on the earth. In the design of irrigation and other hydraulic structures, evaluating the magnitude of extreme rainfall for a specific probability of occurrence is of much importance. For the present study daily rainfall data from 1968-2010 for Waghodia Taluka is collected and analysed for Annual One Day Maximum Rainfall (AODMR) using various five commonly used probability distribution viz., Gumbel’s distributions, Normal distributions, Lognormal, Log Pearson type III and Generalized Extreme distribution to determine the best fit probability distribution. The expected values were compared with the observed values using goodness of fit were determined by chi square (γ2) test. The chisquare values for Normal, Log-Normal, Log- Pearson type-III, Generalized Extreme distributions and Gumbel’s distributions and were 29.98, 29.68, 48.58, 8.40 and 4.06 respectively which shows that the Gumbel’s distribution was the best fit probability distribution to forecast annual one day maximum rainfall for different return periods. Also, expected Annual One Day Maximum Rainfall using Gumbel’s distribution for return period of 2, 5, 10, 25, 50 and 100 were 122.65mm, 177.75mm, 214.24mm, 260.34mm, 294.54mm and 328.49mm respectively. The comparisons between the observed and predicted maximum value of rainfall clearly shows that the developed model can be efficiently used for the prediction of rainfall. The results of this study would be useful for agricultural scientists, decision makers, policy planners and researchers for agricultural development and constructions of small soil and water conservation structures, irrigation and drainage systems in Gujarat, India. Keyword- AODMR, Probability Distributions, Chi-Square Test __________________________________________________________________________________________________
I. INTRODUCTION Analysis of rainfall data strongly depends on its distribution pattern. It has long been a topic of interest in the fields of meteorology in establishing a probability distribution that provides a good fit to daily rainfall. Several studies have been conducted in India and abroad on rainfall analysis and best fit probability distribution function such as normal, lognormal, Gumbel, Weibull and Pearson type distribution were identified by Sharma and Singh (2010). Frequency analysis of rainfall is an important tool for solving various water management problems and is used to assess the extent of crop failure due to deficiency or excess of rainfall. Probability analysis of annual maximum daily rainfall for different returns periods has been suggested for the design of small and medium hydraulic structure (Bhatt et al, 1996). Rainfall modelling is an important area of hydrologic studies and is one in which research is still being actively carried out. Probability analysis can be used for prediction of occurrence of future events from available records of rainfall with the help of statistical methods (Kumar and Kumar, 1989).
II. LITERATURE REVIEW Sabarish et al (2017) studied an extreme value analysis of rainfall for Tiruchirapalli City in Tamil Nadu and best-fit probability distribution was evaluated for 1, 2, 3, 4 and 5 days of continuous maximum rainfall. The goodness of fit was evaluated using Chisquare test. The results of the goodness-of-fit tests indicate that log-Pearson type III method was the overall best-fit probability distribution for 1-day maximum rainfall and consecutive 2-, 3-, 4-, 5- and 6-day maximum rainfall series of present study. Similarly Bhakar et al. (2008) studied the variation of rainfall pattern using Weibull’s (extreme value type III) method and weekly rainfall was predicted at various probability levels. Gumbel distribution was found to be fitted well for prediction of weekly and monthly maximum rainfall. Rahman et al. (1997) used trend analysis to study the changes in monsoon rainfall of Bangladesh and observed no significant changes. Ahmed (1989) estimated the probabilistic rainfall extremes in Bangladesh during the pre-monsoon season.
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