Article

Monalika Malaviya, Parul Institute of Technology; Dr. Vilin Parekh ,Parul Institute of Engineering and Technology

Missing Rainfall data may vary in length from one or two days to several years. Especially in data-sparse areas, estimation of the missing data is necessary in order to utilize partial records. For filling missing rainfall data, various methods are used. To generate one output, some methods need only one input variable like Closest Station Method (CSM) & Artificial Neural Network Method (ANN) and some methods must need more than one input variables like Arithmetic Average Method (AAM), Inverse Distance Method (IDM) & Normal Ratio Method (NRM). Gujarat is divided into eight agroclimatic zones. South Gujarat Agroclimatic zone partly consisting of Bharuch, Navsari and Surat districts is selected for the present study. There are 22 talukas under the study area and 75 rain gauge stations cover selected 3 districts. Daily rainfall data from 1981 to 2015 of respective rain gauge stations are collected from State Water Data Center, Gandhinagar. In order to compute the missing daily rainfall data, the latitudes and longitudes of the different rain gauge stations are converted to x and y co–ordinates using the Franson Coord Trans V 2.3. Cluster analysis is used to group the rain gauge stations into clusters for filling in missing rainfall data. The paper discusses determining missing rainfall data of rain gauge stations of Navsari district by Closest Station Method.