Frequency Analysis of Rainfall Data of Dharamshala Region (original) (raw)
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2021
In this case study, Daily Rainfall Data (1984-2019) of SambraRaingauge station in North Karnataka is used. An attempt was made to fit various probability distribution functions to the datasets of 1 day and 2 to 5 consecutive days annual maximum rainfall. The goodness of fit of probability distribution functions were tested by comparing the Chi-square (χ 2) values. No single probability distribution was adequate to describe the entire datasets. Various trendlines were also fitted to the rainfall datasets mentioned above; the best fit was decided based on the value of coefficient of determination R 2 , no single trendline equation was able to describe the entire datasets. The magnitudes of 1 day as well as 2 to 5 consecutive days annual maximum rainfall corresponding to 2 to 100 years return period were estimated best fit distribution function, it was found that even though Normal distribution function had low Chi-square value comparatively, it cannot be used overall for estimation of rainfall values of different return periods for all the datasets. Rainfall was also estimated by best fit trendline equation i.e.polynomial 3 rd order, for all the datasets corresponding to 2 to 100 years return period. It was observed the rainfall values predicted for 100 years return period for 1 to 5 consecutive days maximum rainfall were extremely high and unrealistic with respect to climate conditions of Sambra region. Chi-square test (χ 2) was conducted between observed rainfall and predicted rainfall by different trendline equations to ascertain the bestfit as determined by R 2 , it was not able to establish the same results as determined by coefficient of determination.
Frequency analysis of consecutive days maximum rainfall at Banswara, Rajasthan India
ARPN J. Eng. Appl. …, 2006
Annual one day maximum rainfall and two to five days consecutive days maximum rainfall corresponding to return period varying from 2 to 100 years are used by design engineers and hydrologists for the economic planning, design of small and medium hydrologic structures and determination of drainage coefficient for agricultural fields. A maximum of 154.31mm in 1 day, 250.88mm in 2 days, 270.15mm in 3 days, 284.18mm in 4 days and 295.54mm in 5 days is expected to occur at Udaipur, Rajasthan every 2 years. For a recurrence interval of 100 years, the maximum rainfall expected in 1 day, 2, 3, 4 and 5 days is 773.6mm, 849.34mm, 874.19mm, 931.78mm and 957.89mm, respectively. The magnitudes of 1 day as well as 2 to 5 consecutive days annual maximum rainfall corresponding to 2 to 100 years return period were estimated using Gamma function. Various probability distributions and Transformations can be applied to estimate one day and two to five consecutive days annual maximum rainfall of various return periods. Three commonly used probability distributions (viz: Normal, Log Normal and Gamma distribution) were tested by comparing the Chi-square value. Gamma distribution was found to be best fit for the region.
The daily rainfall data of 39 years (1973-2011) were analyzed to determine the annual one day maximum rainfall of Jhalarapatan area of Rajasthan. The observed values were estimated by Weibull’s plotting position and expected values were estimated by four well known probability distribution functions viz., normal, log-normal, log-Pearson type-III and Gumbel. The expected values were compared with the observed values and goodness of fit were determined by chi-square ( ) test. The results showed that the log-Pearson type-III distribution was the best fit probability distribution to forecast annual one day maximum rainfall for different return periods. Based on the best fit probability distribution, the minimum rainfall of 44.74 mm in a day can be expected to occur with 99 per cent probability and one year return period and maximum of 252.98 mm rainfall can be received with one per cent probability and 100 year return period. The results of this study would be useful for agricultural sc...
International Journal of Current Microbiology and Applied Sciences, 2019
Rainfall distribution pattern has considerable impact on agriculture sector of Asia Pacific region. The extreme events like floods, droughts frequently occur as a result of growth in population, increased urbanization and decreased intensity of rainfall and forest area. The different continuous probability are used in hydrological studies such as release water from water reservoirs from high level
Probability distributions for long term rainfall data in Ludhiana - A case study
MAUSAM
The rainfall data of Ludhiana for a period of 32 years covering 1981 to 2012 have been collected from School of Climate Change and Agricultural Meteorology. The study was planned to find the rainfall variability and amount of rainfall at different probability levels for the year 1981-2012. The rainfall data was analyzed on weekly basis to work out the initial & conditional probability for rainfall at different levels, i.e., > 5 mm, > 10 mm, > 20 mm, > 30 mm, > 40 mm and > 50 mm using Markov chain model. In addition to this, incomplete gamma distribution was also used to find out the occurrence of rainfall events at different probability levels, i.e., 20, 30, 40, 50, 65 and 75 per cent. The study results in estimation of maximum and minimum initial probability and conditional probability (wet and dry) for standard meteorological week. The results will be useful for deciding the sowing time, irrigation/fertilizer scheduling and harvesting time for different crops. In addition to this study will be useful for determining the runoff volume, peak runoff rate and hence can be used for designing of rainwater harvesting structures.
GRD Journals, 2019
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 chi-square 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.
Probability Analysis for prediction of rainfall of Raipur region (Chhattisgarh)
The Allahabad Farmer
Consecutive days of annual maximum rainfall data corresponding to different return periods are required for economic planning and design of hydraulic structures like small dams, bridges, culverts, drainage works etc. Different probability distribution models namely, Normal, Log Normal, Log Pearson type III and Gumbel were tested for Raipur Region by comparing the Chi-square values. The Gumbel distribution was found to be fit best for one day annual maximum rainfall. Log Pearson type III distribution was found to be best fitted for two, three and four consecutive days of annual maximum rainfall. Normal distribution was found to be best fitted for five consecutive days annual maximum rainfall. The one day annual maximum rainfall and two to five consecutive days annual maximum rainfall exhibited strong Linear relationships (R2 = 0.9191 to 0.9494). The regression equations developed in the present studies can be successfully used for prediction of rainfall of consecutive days ranging from two to five annual maximum rainfalls with one day annual maximum rainfall for Raipur region.
E3S Web of Conferences
India being a Tropical country, experiences variety Rainfall. The Rainfall is the prime input required for Design of Hydraulic structures like culverts and bridges, Irrigation canals, storm water and road drainage system, etc. To estimate the detailed design and planning of crops, statistical analysis is required. In the present study, annual rainfall data for a period of 20 years (2000 to 2019) is used to understand the statistical behavior of the rainfall in last two decades for Solapur district. Also return period by various plotting position formulae is evaluated from the annual rainfall. The rainfall variability is checked by calculating the mean, standard deviation and coefficient of variation. From the study results, the rainfall pattern is found to be irregular. The best fitted probability distribution was identified based on the minimum deviation between actual and estimated values.
The present study was conducted for probability analysis of previous 20 years (1993-2012) with the prime objective for the prediction of annual maximum rainfall of one to five consecutive days of Ambedker Nagar (Tanda). The observed values were computed by wei-bull's formula. The maximum rainfall values were estimated by proposed predicted models viz. Gumbel, Log Pearson Type III Log Normal and Gamma. The predicted and observed values were also established. The rainfall data has been in the above distributions and their corresponding rainfall events were estimated at 9.52, 23.81, 47.62 and 95.24 percent probabilities level. The goodness of fit models was tested by Chi-square formula proposed by Hogg and Tanis. 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 Log normal distribution was found to be best model for prediction by Gumbel distribution shows very close relation to be observed rainfall for two consecutive day's annual maximum rainfall (mm).