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Probable Maximum Precipitation (PMP) is used for estimating Probable Maximum Flood (PMF) which, in turn, is used for design of major hydraulic structures, such as dams and spillways, flood protection works, and nuclear power plants. One of the commonly used methods for estimating PMP is the statistical method, also called Hershfield method that entails computation of frequency factor, adjustment of the frequency factor, construction of an enveloping curve of the frequency factor, estimation of PMP, choosing a probability distribution of PMP, and determination of the return period of PMP. There are, however, uncertainties associated with the PMP values estimated using the statistical method. This study determined the PMP values for different durations using the statistical method with data from the Brazos River basin, Texas. It was found that significant uncertainty in the PMP estimates can occur with the use of enveloping curve of the frequency factor and the number of stations involved in its construction. Hershfield's curve yielded higher frequency factor values by 16% for 1 hour duration, by 17.9% for 6 hour duration, and by 22.1% for 24 hour duration. In comparison with basin-specific values, the PMP values from the Hershfield enveloping curve were 16.8% higher for 1-hour duration, 18.5% for 6-hour duration, and 23.4% for 24-hour duration. For most of the Brazos River basin the return period of the PMP values was in the range of 1000 to 3000 years which was less than the range of 103 to 106 years reported in HMR 51, showing the degree of risk associated with the PMP values. Therefore, a basin specific-enveloping curve is suggested. From 24 commonly used statistical distributions and 5 goodness of fit tests, the Burr Type XII distribution was found to be the best frequency distribution for describing PMP. It was observed that the return period obtained from the Burr type XII frequency distribution was not significantly higher than that obtained from the hydreometeorological reports (HMRs) of National Weather Service and other studies. .

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SUMMARY :The study was carried out to develop the classificatory statistical model to predict and classify the farmers into adopters and non-adopters in Kolar district of Karnataka for the year 2013. Linear discriminant analysiswas carried out by considering the various socio-economic characteristics of farmers as predictors and adoption behaviour of the farmers as response variable in order to assess the factors influencing on adoption of drought coping mechanisms. The result shows that the Box's M test is 161.3 with their F approximation 1.83 is non-significant (0.19) at 5% level of significance, Eigen value (2.51) of the first function explains 100% of variations in the data which is potential enough to classifying the groups, wilk's lambda associated with the function (=0.28) is transforms to a chi square of 140.82 with 12 DF, which is statistically significant and the following variables such asFarm Size (0.552), Extension Visits (0.574), Crop Diversification (0.321) and Crop Insurance (0.368) are relatively more important and positively influencing on discrimination of farmers group. Whereas the variable like Age (-0.516) negatively influencing on discrimination of adopters and non-adopters.

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Flooding, caused by extreme precipitation, including probable maximum precipitation (PMP), causes considerable loss almost yearly in many parts of the U.S., such as Texas and Louisiana. A common method for estimating PMP is the statistical method but it involves sample mean, standard deviation, and frequency factor each of which can be considered as a random variable. As the uncertainty in the PMP values is due to the uncertainty in sample mean, sample standard deviation, and frequency factor, the relative contribution of each random variable to the total uncertainty was determined. The best-fit probability distribution was found and the hazard rate was calculated for different probability distributions. Using the best-fit probability distribution, design risk estimates along with probability bounds on the PMP values were determined. Considering the damage (in U.S. dollars) a PMP event can cause, risk analysis of extreme precipitation was done. The damage due to a single PMP event of 12-hour duration can be as high as $2 billion in Harris County, Texas.

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