Determining Missing Rainfall Data of Rain Gauge Stations in South Gujarat Agroclimatic Zone by Close

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GRD Journals | Global Research and Development Journal for Engineering | Emerging Research and Innovations in Civil Engineering (ERICE - 2019) | February 2019

e-ISSN: 2455-5703

Determining Missing Rainfall Data of Rain Gauge Stations in South Gujarat Agroclimatic Zone by Closest Station Method: Special Reference to Navsari District 1Monalika

Malaviya 2Dr. Vilin Parekh 2 Principal 1 Department of Civil Engineering 1,2 Parul Institute of Engineering and Technology, Vadodara, Gujarat, India Abstract 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. Keyword- Rain Gauge Stations, Rainfall Data, Missing Data, Cluster Analysis, Closest Station Method __________________________________________________________________________________________________

I. INTRODUCTION At least no one rain gauge station is available with full of rainfall data so it is necessary to find missing rainfall data. For that, different methods are available. Some methods need one input variable means rain gauge station to generate output means rainfall data and some methods must need more than one input variables to generate output one. Different researchers used different methods for determining missing rainfall data. Demirhan and Renwick (2018) focused on the estimation of missing solar irradiance values and for that, they used durations like minutely, hourly, daily, and weekly. An extensive number of imputation methods were used in solar irradiance series. They compared the accuracy of 36 imputation methods. Records of rainfall were examined by Miro et al. (2017). Multiple Imputation Methods like 6 Linear, 2 Non-linear and 2 Hybrid Methods were used for finding missing rainfall data. Daily rainfall data of 60 years were considered. Sattari et al. (2016) used Arithmetic Averaging Method (AA), Non-linear Regression Method (NR), Linear Regression Method (LR) and Multiple Linear Regression Method (MLR) methods for finding missing rainfall data. In this study, monthly rainfall data of 29 years from 6 rain gauge stations considered. Regression method was used by Khalifeloo et al. (2015) for filling missing hydrological records. Dumedah et al. (2014) introduced artificial neural networks and statistical methods for infilling missing soil moisture records of 13 monitoring stations. Ghuge and Regulwar (2013) used Artificial Neural Network (ANN) Method for monthly rainfall data of 10 years from 6 rain gauge stations. Nkuna and Odiyo (2011) examined records of 1 year rainfall data from 5 rain gauge stations and determined the missing rainfall data. For finding missing rainfall data, Artificial Neural Network (ANN) method was used. Kim and Pachepsky (2010) were used Artificial Neural Network (ANN) method for infilling missing precipitation data for 7 years from 39 weather stations. Patel et al. (2008) concluded the effectiveness of the artificial neural network method for Mehsana district, Gujarat, India, compared to the arithmetic average method, inverse square distance (ISD) (National Weather Service method), normal ratio method, linear and multiple regression methods.

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