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Chaudhary Charan Singh Haryana Agricultural University, Hisar

Chaudhary Charan Singh Haryana Agricultural University popularly known as HAU, is one of Asia's biggest agricultural universities, located at Hisar in the Indian state of Haryana. It is named after India's seventh Prime Minister, Chaudhary Charan Singh. It is a leader in agricultural research in India and contributed significantly to Green Revolution and White Revolution in India in the 1960s and 70s. It has a very large campus and has several research centres throughout the state. It won the Indian Council of Agricultural Research's Award for the Best Institute in 1997. HAU was initially a campus of Punjab Agricultural University, Ludhiana. After the formation of Haryana in 1966, it became an autonomous institution on February 2, 1970 through a Presidential Ordinance, later ratified as Haryana and Punjab Agricultural Universities Act, 1970, passed by the Lok Sabha on March 29, 1970. A. L. Fletcher, the first Vice-Chancellor of the university, was instrumental in its initial growth.

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20211
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Subject: Statistics × Author: Nain, Mohit × End date: 2021 × Start date: 2020 ×
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    ThesisItemOpen Access
    Regional Frequency Analysis of Extreme Rainfall Using L-moments and Partial L-moments in Haryana
    (CCSHAU, Hisar, 2021-06) Nain, Mohit; HOODA, B.K.
    Regional frequency analysis (RFA) is of great importance for planning and designing hydraulic structures by policymakers and structural engineers. In this study, we focus on regional frequency analysis of daily and monthly extreme rainfall from 1970–2017 at 27 rain gauge stations of Haryana (India) using L-moments and PL-moments. Based on mean monthly rainfall, these 27 rain gauge stations were grouped into three homogeneous regions (Region-I, Region-II, and Region-III) using Ward‟s method of cluster analysis and homogeneity of each region was tested using heterogeneity measures (H). The best fit regional distribution was selected for each region from the five candidate distributions i.e. GEV, GNO, GLO, GPA, and PE3 using the -statistic and L-and PL-moments ratio diagrams. For maximum monthly rainfall, using L-moments method, it was found that GNO was best-fitted for Region-I and Region-II while PE3 for Region-III. For maximum daily rainfall, for Region-I, Region-II and Region-III; PE3, GEV, and GLO was the best-fitted distribution, respectively. Using PL-moments method, for Region-I, for maximum monthly rainfall, GNO was best fitted. For Region-II, GEV was best fitted and PE3 for Region-III. Quantiles for various return periods were estimated using these best-fitted distributions for each region. The performance of both methods i.e. L-moments and PL-moments in quantiles estimation were studied by Monte Carlo simulations. From these simulations, accuracy measures such as relative RMSE and absolute relative bias were calculated and it was observed that these accuracy measures were smaller in the case of PL-moments as compared to L-moments. Also, quantiles were estimated using the regional and at-site base approach. The performance of regional and at-site based rainfall quantiles was studied in terms of relative RMSE. It was observed that regional analysis provided better estimates of the quantiles compared to the at-site based estimation.